159 results on '"Image texture"'
Search Results
2. The Use of Low-Dose CT Intra- and Extra-Nodular Image Texture Features to Improve Small Lung Nodule Diagnosis in Lung Cancer Screening
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Nagina Malguria, Arman Rahmim, Jon A. Steingrimsson, Junghoon Lee, Amira Hussien, Rongkai Yan, Saeed Ashrafinia, Linda C. Chu, Cheng Ting Lin, Peng Huang, and Seyoun Park
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COPD ,medicine.medical_specialty ,Lung ,business.industry ,Cancer ,medicine.disease ,Malignancy ,03 medical and health sciences ,0302 clinical medicine ,medicine.anatomical_structure ,Image texture ,030220 oncology & carcinogenesis ,medicine ,National Lung Screening Trial ,030212 general & internal medicine ,Radiology ,Lung cancer ,business ,Lung cancer screening - Abstract
–Standard computed tomography (CT) scan is performed on lung cancer patients for progression and lesion classification. However, low-dose CT (LDCT) is commonly used in lung cancer screening for high-risk people. Extensive studies have shown that computer-aided diagnosis (CAD) using standard CT could greatly improve the diagnostic accuracy of early lung cancer. Unlike standard CT imaging, the application of radiological texture features extracted by radiologists on LDCT imaging is not well established due to lower resolution and higher variations. The purpose of this study is to investigate possible diagnosis value of texture features by comparing the classification performance of radiologic reading with radiologic reading combined with computer-aided texture features. A total of 186 biopsy-confirmed control and lung cancer cases were obtained from the National Lung Screening Trial (NLST). Cases were matched by various clinical parameters including age, gender, smoking status, chronic obstructive pulmonary disease (COPD) status, body mass index (BMI) and image appearances. We compared the subjective diagnosis of benign/malignant with the consensus readings of three radiologists. We then developed a CAD framework that imports radiologic reading features and extracts CAD features for heterogeneity quantification and data analysis. A total of 1342 CAD features were extracted. After eliminating highly correlated and redundant features, the remaining 458 features were given to a random forest algorithm, and a predicted probability of malignancy score (Pm) was calculated. Patients were grouped into 140 training (70 biopsypositive for cancer and 70 negatives) and 46 testing (20 positives and 26 negatives) sets, and a threshold value over Pm (0.5) was then used to classify the test set into cancer and non-cancer. Clinical accuracy [sensitivity, specificity, positive predictive value (PPV), and negative predictive value (PV)] were [0.95, 0.88, 0.86, 0.96] and [0.70, 0.69, 0.64, 0.75] for the CAD and radiologic reading, respectively. The CAD framework incorporating the clinical reading with the texture features extracted from LDCT increased the PPV and reduced the false positive (FP) rate in the early diagnosis of lung cancer. This approach shows promise for improving the accuracy of lung cancer diagnosis in the clinical environment, especially in patients with well-established risk factors.
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- 2017
3. The value of CT image texture analysis in early assessment of chemotherapeutic effect in patients with advanced lung adenocarcinoma: A preliminary study
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Lin, Xi
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Computer Applications-Detection, diagnosis ,Retrospective ,Performed at one institution ,Chest ,Chemotherapy ,Diagnostic or prognostic study ,Thorax ,Respiratory system ,Lung ,Cancer ,CT - Abstract
Purpose Methods and materials Results Conclusion Personal information and conflict of interest References, Purpose: To investigate the value of the gray level co-occurrence matrix(GLCM) parameters of computed tomography-based texture analysis(CTTA) to predict and evaluate theearly efficacyof chemotherapy in patients with advanced lung...
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- 2020
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4. Study Results from Osaka Medical Center for Cancer and Cardiovascular Diseases Update Understanding of Gliomas (Introduction of High Throughput Magnetic Resonance T2-Weighted Image Texture Analysis for WHO Grade 2 and 3 Gliomas)
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Physical fitness ,Medical centers ,Cancer ,Cancer research ,Health - Abstract
2017 MAR 25 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- New research on Oncology - Gliomas is the subject of a report. [...]
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- 2017
5. Gtexture: Novel Extension of Image Texture Analysis to Graphs and Its Application to Cancer Informatics (Updated June 2, 2023).
- Abstract
"To demonstrate the utility of these metrics in cancer biology, we demonstrate these metrics can distinguish different fitness landscapes, gene co expression and regulatory networks, and protein interaction networks with both simulated and publicly available experimental gene expression data. Network-based data underlies drug discovery, drug response prediction, and single-cell dynamics and thus these metrics provide an additional tool for tackling these problems in cancer." "We demonstrate that graph texture varies across multiple network types including fitness landscapes and large protein interaction networks with experimental expression data. [Extracted from the article]
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- 2023
6. Novel technique distinguishes between types of oral bone lesion based on MRI scan image texture.
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Keywords: Ameloblastomas; Bone Research; Cancer; Dentistry; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo; Health and Medicine; Hospitals; Oncology EN Ameloblastomas Bone Research Cancer Dentistry Fundacao de Amparo a Pesquisa do Estado de Sao Paulo Health and Medicine Hospitals Oncology 660 660 1 03/24/23 20230228 NES 230228 2023 FEB 28 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Ameloblastomas and odontogenic keratocysts are benign lesions of the maxillo-mandibular region with different biological characteristics. Because texture analysis is based on the signal intensity of pairs of pixels, the results will be better if there are more patients in the study sample and the lesions are larger. [Extracted from the article]
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- 2023
7. Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients
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Yue, Yong, Osipov, Arsen, Fraass, Benedick, Sandler, Howard, Zhang, Xiao, Nissen, Nicholas, Hendifar, Andrew, and Tuli, Richard
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Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Cancer ,Rare Diseases ,Biomedical Imaging ,Clinical Research ,Patient Safety ,4.1 Discovery and preclinical testing of markers and technologies ,Detection ,screening and diagnosis ,PET/CT ,Pancreatic adenocarcinoma ,image texture ,radiomics ,therapy response ,Clinical sciences ,Oncology and carcinogenesis - Abstract
BackgroundTo stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients.MethodsTwenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis.ResultsMedian age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (
- Published
- 2017
8. Fast and accurate vision-based stereo reconstruction and motion estimation for image-guided liver surgery
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Andrew D. Speers, Burton Ma, William R. Jarnagin, Sharifa Himidan, Amber L. Simpson, and Richard P. Wildes
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phantoms ,image matching ,stereo image processing ,image reconstruction ,computerised tomography ,tumours ,motion estimation ,liver ,image registration ,medical image processing ,surgery ,cancer ,image texture ,time series ,accurate vision-based stereo reconstruction ,image-guided liver surgery ,ablation ,adjacent complex vasculature ,end-to-end solution ,coarse-to-fine stereo approach ,liver imaging ,three-dimensional boundary recovery ,robust 3D motion estimator ,adaptive CTF matching approach ,liver phantom ,precise quantitative evaluation ,liver resection ,fast vision-based stereo reconstruction ,oncologic outcome ,low texture regions ,adaptive windows ,volumetric computed tomography scan ,Medical technology ,R855-855.5 - Abstract
Image-guided liver surgery aims to enhance the precision of resection and ablation by providing fast localisation of tumours and adjacent complex vasculature to improve oncologic outcome. This Letter presents a novel end-to-end solution for fast stereo reconstruction and motion estimation that demonstrates high accuracy with phantom and clinical data. The authors’ computationally efficient coarse-to-fine (CTF) stereo approach facilitates liver imaging by accounting for low texture regions, enabling precise three-dimensional (3D) boundary recovery through the use of adaptive windows and utilising a robust 3D motion estimator to reject spurious data. To the best of their knowledge, theirs is the only adaptive CTF matching approach to reconstruction and motion estimation that registers time series of reconstructions to a single key frame for registration to a volumetric computed tomography scan. The system is evaluated empirically in controlled laboratory experiments with a liver phantom and motorised stages for precise quantitative evaluation. Additional evaluation is provided through testing with patient data during liver resection.
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- 2018
- Full Text
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9. Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening
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Rashmi Panda, N.B. Puhan, and Ganapati Panda
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image texture ,medical image processing ,image segmentation ,tumours ,cancer ,blood vessels ,neurophysiology ,biomedical optical imaging ,mean curvature ,texture constrained composite weighted random walk algorithm ,optic disc segmentation ,cup-disc ratio-based glaucoma screening ,fundus imaging ,OD boundary ,blood vessel occlusion ,random walk algorithm ,Gabor texture energy features ,composite weight function ,edge weights ,deformable model-based OD segmentation techniques ,curve initialisation ,local energy minima problem ,DRIVE database images ,DIARETDB1 database images ,DRISHTI-GS database images ,MESSIDOR database images ,mean absolute distance ,overlapping ratio ,dice coefficient ,quantitative performance ,Medical technology ,R855-855.5 - Abstract
Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.
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- 2018
- Full Text
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10. Mammographic Classification Based on XGBoost and DCNN With Multi Features
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Runyu Song, Taoying Li, and Yan Wang
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0301 basic medicine ,General Computer Science ,deep convolution neural network ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Convolutional neural network ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,Image texture ,Histogram ,medicine ,General Materials Science ,business.industry ,Deep learning ,General Engineering ,Cancer ,Pattern recognition ,medicine.disease ,Support vector machine ,Statistical classification ,ComputingMethodologies_PATTERNRECOGNITION ,030104 developmental biology ,Feature (computer vision) ,computer-aided diagnosis ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,mammograms classification ,business ,lcsh:TK1-9971 ,Classifier (UML) ,030217 neurology & neurosurgery - Abstract
The classification of benign and malignant masses in mammograms by Computer-Aided Diagnosis (CAD) is one of the most difficult and important tasks in the development of CAD systems. This classification has commonly been automated by extracting a set of handcrafted features from mammograms and relating the responses to breast cancer. Recently, the application of Deep Learning (DL) technology in medical imaging informatics has been attracting extensive research interest. However, limited medical image datasets and feature expression often reduce the performance of DL-based schemes. Therefore, this study aims to develop a new combined feature CAD method based on DL for classifying mammographic masses into three classes: normal, benign and cancer (malignant) masses. Three kinds of breast masses were scored by using Deep Convolution Neural Network (DCNN) as a feature extractor. Then the scoring features are combined with the image texture features as input to the classifier. This features including the scoring features, Gray-Level Co-occurrence Matrix (GLCM) and Histogram of Oriented Gradient (HOT) were employed to extract the breast mass information in mammograms and the classifier of Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were trained for the classification task. Accuracy (ACC), Precision (Pre), Recall (Rec), F1-score (F1), and Overall Accuracy (Overall ACC) are used to evaluate the performance of the proposed system and the results show that the proposed multi-features combination model performs the best results. The performance of the XGBoost classifier has proved to be better in comparison to the SVM classification algorithms. As a result, when XGBoost was used as a classifier, the correct identification rate of the Overall ACC was 92.80% and that of malignant tumors was 84%, with reasonable and best results. These results indicate that the proposed method may help in more accurately diagnosing cases that are difficult to classify on images.
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- 2020
11. Bladder cancer organoid image analysis: textured-based grading
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Mahesh Devarasetty, Seda Camalan, M. Khalid Khan Niazi, Shay Soker, and Metin N. Gurcan
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Tumor microenvironment ,Stromal cell ,Bladder cancer ,business.industry ,Cancer ,medicine.disease ,medicine.disease_cause ,Extracellular matrix ,Image texture ,medicine ,Cancer research ,Field cancerization ,business ,Carcinogenesis - Abstract
Bladder cancer has high recurrence rates, which leads to treatment difficulties and reduced survival. Field cancerization is the prevailing idea for why bladder cancers recur with high frequency, and it centers around genetic and epigenetic changes in tissue that lead to conditions favoring recurrence. However, the specifics of these alterations are not well understood or described. The tumor microenvironment (TME) has been implicated as a strong proponent of oncogenesis in many organ systems, including the bladder. The TME comprises stromal cells, paracrine factors, and extracellular matrix (ECM) components, which may contribute to field cancerization. As such, identifying the hallmarks of these alterations may expedite the prognosis of recurrence. For this purpose, we fabricated bladder cancer organoids of varied cancer grades, with which we developed a texture-based grading system. Image texture is characterized by filtering images and finding their similarity. The similar images are clustered, and the cumulative histogram of clusters is formed to find the closest training image. In two independent image data sets of 54 and 76 images, respectively, with different imaging protocols, 100% and 92% accuracy were achieved.
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- 2021
12. Role of texture analysis in breast MRI as a cancer biomarker: A review
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Rhea Chitalia and Despina Kontos
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Oncology ,medicine.medical_specialty ,Breast Neoplasms ,Disease ,Article ,Pattern Recognition, Automated ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,Image texture ,Internal medicine ,Image Interpretation, Computer-Assisted ,Image Processing, Computer-Assisted ,medicine ,Humans ,Breast MRI ,Radiology, Nuclear Medicine and imaging ,Breast ,Stage (cooking) ,skin and connective tissue diseases ,Models, Statistical ,medicine.diagnostic_test ,business.industry ,Cancer ,Evidence-based medicine ,Image Enhancement ,Prognosis ,medicine.disease ,Magnetic Resonance Imaging ,Biomarker (medicine) ,Female ,business ,Biomarkers - Abstract
Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis for breast cancer. MRI can provide a whole-tumor sampling of disease burden and is widely utilized in clinical care. Texture analysis can provide a localized description of breast cancer, with particular emphasis on quantifying breast lesion heterogeneity. The object of this review is to provide an overview of texture analysis applications towards breast cancer diagnosis, prognosis, and treatment response evaluation and review the role of image-based texture features as noninvasive prognostic and predictive biomarkers. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:927-938.
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- 2018
13. Automated air region delineation on MRI for synthetic CT creation
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X. Allen Li, Ergun Ahunbay, Xinfeng Chen, H. Nasief, and Ranjeeta Thapa
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Computer science ,Image processing ,computer.software_genre ,030218 nuclear medicine & medical imaging ,Automation ,03 medical and health sciences ,0302 clinical medicine ,Image texture ,Voxel ,Image Processing, Computer-Assisted ,medicine ,Humans ,Dosimetry ,Radiology, Nuclear Medicine and imaging ,Radiometry ,Radiation treatment planning ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,Air ,Cancer ,Bayes Theorem ,Magnetic resonance imaging ,medicine.disease ,Magnetic Resonance Imaging ,Abdominal Neoplasms ,030220 oncology & carcinogenesis ,Tomography ,Tomography, X-Ray Computed ,computer ,Algorithms ,Biomedical engineering - Abstract
Automatically and accurately separating air from other low signal regions (especially bone, liver, etc) in an MRI is difficult because these tissues produce similar MR intensities, resulting in errors in synthetic CT generation for MRI-based radiation therapy planning. This work aims to develop a technique to accurately and automatically determine air-regions for MR-guided adaptive radiation therapy. CT and MRI scans (T2-weighted) of phantoms with fabricated air-cavities and abdominal cancer patients were used to establish an MR intensity threshold for air delineation. From the phantom data, air/tissue boundaries in MRI were identified by CT-MRI registration. A formula relating the MRI intensities of air and surrounding materials was established to auto-threshold air-regions. The air-regions were further refined by using quantitative image texture features. A naive Bayesian classifier was trained using the extracted features with a leave-one-out cross validation technique to differentiate air from non-air voxels. The multi-step air auto-segmentation method was tested against the manually segmented air-regions. The dosimetry impacts of the air-segmentation methods were studied. Air-regions in the abdomen can be segmented on MRI within 1 mm accuracy using a multi-step auto-segmentation method as compared to manually delineated contours. The air delineation based on the MR threshold formula was improved using the MRI texture differences between air and tissues, as judged by the area under the receiver operating characteristic curve of 81% when two texture features (autocorrelation and contrast) were used. The performance increased to 82% with using three features (autocorrelation, sum-variance, and contrast). Dosimetric analysis showed no significant difference between the auto-segmentation and manual MR air delineation on commonly used dose volume parameters. The proposed techniques consisting of intensity-based auto-thresholding and image texture-based voxel classification can automatically and accurately segment air-regions on MRI, allowing synthetic CT to be generated quickly and precisely for MR-guided adaptive radiation therapy.
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- 2020
14. Texture analysis of masses malignant in mammograms images using a combined approach of diversity index and local binary patterns distribution
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Simara Vieira da Rocha, Anselmo Cardoso de Paiva, Marcelo Gattass, Aristófanes Corrêa Silva, and Geraldo Braz Junior
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Computer science ,Local binary patterns ,02 engineering and technology ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,Image texture ,Artificial Intelligence ,Region of interest ,Histogram ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Mammography ,Computer vision ,medicine.diagnostic_test ,Receiver operating characteristic ,business.industry ,General Engineering ,Cancer ,Pattern recognition ,medicine.disease ,Computer Science Applications ,Support vector machine ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
A methodology to texture analysis of masses in digitized mammography is proposed.Our methodology uses only the texture analysis for recognition.We provide the specialist bigger support to the diagnosis of breast cancer.We cooperate to a more precise diagnosis and support in the medical intervention. A World Health Organization (WHO) report estimates that in 2015, at least 561 thousand women will die of breast cancer. Although breast cancer is considered a disease of the developed world, nearly 50% of the cases and 58% of the deaths occur in the less developed countries. A mammogram is a way of discovering not just the palpable tumors that cause cancer but also other lesions that are not perceived during the physical examination performed by the expert physician or during self-exams; however, it is known that this exam is targeted for women after the age of 40 because age is one of the factors that can cause great variations in sensitivity during the exam. Besides the patient's age, the expert's experience and the quality of the images obtained during the exam are decisive factors in the detection of breast cancer. This work presents two novelties. The first is the use of Local Binary Patterns (LBPs) to generate a representation of a Region of Interest (ROI) image. Over this representation, we generate other representations using techniques such as image histograms, gray-level co-occurrence matrices (GLCMs) and gray-level run-length matrices (GLRLMs). These representations allow texture analysis through several perspectives. The second novelty uses these representations as input to the application of indexes adapted from ecology (Shannon, McIntosh, Simpson, Gleason and Menhinick) as texture descriptors. Based on this strategy, we analyze mammographic image textures to classify regions of these images as benign or malignant using a Support Vector Machine (SVM). The best result achieved was of 88.31% accuracy, 85% sensitivity, 91.89% specificity, a positive probability ratio of 10.48, a negative probability ratio of 0.16, and an area under the Receiver Operating Characteristic (ROC) curve of 0.88, obtained through the Shannon index. We believe that the proposed method, with some adaptations, may also be used for image texture analysis of several different lesions such as lung nodules, glaucoma and prostates. This belief is based on the achieved results and the method's simplicity.
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- 2016
15. ImHistNet: Learnable Image Histogram Based DNN with Application to Noninvasive Determination of Carcinoma Grades in CT Scans
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Mohammad Arafat Hussain, Rafeef Garbi, and Ghassan Hamarneh
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medicine.medical_specialty ,business.industry ,Cancer ,medicine.disease ,030218 nuclear medicine & medical imaging ,Fuhrman Grade ,03 medical and health sciences ,0302 clinical medicine ,Image texture ,Renal cell carcinoma ,030220 oncology & carcinogenesis ,Carcinoma ,medicine ,Feature based ,Radiology ,business ,Radiation treatment planning ,Image histogram - Abstract
Renal cell carcinoma (RCC) is the seventh most common cancer worldwide, accounting for an estimated 140,000 global deaths annually. Clear cell RCC (ccRCC) is the major subtype of RCC and its biological aggressiveness affects prognosis and treatment planning. An important ccRCC prognostic predictor is its ‘grade’ for which the 4-tiered Fuhrman grading system is used. Although the Fuhrman grade can be identified by percutaneous renal biopsy, recent studies suggested that such grades may be non-invasively identified by studying image texture features of the ccRCC from computed tomography (CT) data. Such image feature based identification currently mostly relies on laborious manual processes based on visual inspection of 2D image slices that are time-consuming and subjective. In this paper, we propose a learnable image histogram based deep neural network approach that can perform the Fuhrman low (I/II) and high (III/IV) grade classification for ccRCC in CT scans. Validated on a clinical CT dataset of 159 patients from the TCIA database, our method classified ccRCC low and high grades with 80% accuracy and 85% AUC.
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- 2019
16. Fast and accurate vision-based stereo reconstruction and motion estimation for image-guided liver surgery
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Sharifa Himidan, Richard P. Wildes, Burton Ma, Amber L. Simpson, William R. Jarnagin, and Andrew Speers
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computerised tomography ,Computer science ,image matching ,tumours ,adaptive windows ,medical image processing ,liver imaging ,030218 nuclear medicine & medical imaging ,motion estimation ,surgery ,0302 clinical medicine ,Health Information Management ,Image texture ,Computer vision ,image texture ,oncologic outcome ,Special Issue: Papers from the 12th Workshop on Augmented Environments for Computer-Assisted Interventions ,Estimator ,image reconstruction ,robust 3D motion estimator ,coarse-to-fine stereo approach ,lcsh:R855-855.5 ,030220 oncology & carcinogenesis ,liver resection ,end-to-end solution ,three-dimensional boundary recovery ,lcsh:Medical technology ,Matching (graph theory) ,Image registration ,phantoms ,Health Informatics ,Iterative reconstruction ,stereo image processing ,adaptive CTF matching approach ,liver ,ablation ,Imaging phantom ,03 medical and health sciences ,Motion estimation ,cancer ,image-guided liver surgery ,adjacent complex vasculature ,volumetric computed tomography scan ,precise quantitative evaluation ,low texture regions ,business.industry ,liver phantom ,image registration ,accurate vision-based stereo reconstruction ,Key frame ,Artificial intelligence ,fast vision-based stereo reconstruction ,time series ,business - Abstract
Image-guided liver surgery aims to enhance the precision of resection and ablation by providing fast localisation of tumours and adjacent complex vasculature to improve oncologic outcome. This Letter presents a novel end-to-end solution for fast stereo reconstruction and motion estimation that demonstrates high accuracy with phantom and clinical data. The authors' computationally efficient coarse-to-fine (CTF) stereo approach facilitates liver imaging by accounting for low texture regions, enabling precise three-dimensional (3D) boundary recovery through the use of adaptive windows and utilising a robust 3D motion estimator to reject spurious data. To the best of their knowledge, theirs is the only adaptive CTF matching approach to reconstruction and motion estimation that registers time series of reconstructions to a single key frame for registration to a volumetric computed tomography scan. The system is evaluated empirically in controlled laboratory experiments with a liver phantom and motorised stages for precise quantitative evaluation. Additional evaluation is provided through testing with patient data during liver resection.
- Published
- 2018
17. Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening
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Niladri B. Puhan, Ganapati Panda, and Rashmi Panda
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Weight function ,Computer science ,mean absolute distance ,02 engineering and technology ,tumours ,overlapping ratio ,biomedical optical imaging ,medical image processing ,DIARETDB1 database images ,030218 nuclear medicine & medical imaging ,local energy minima problem ,0302 clinical medicine ,Health Information Management ,Image texture ,0202 electrical engineering, electronic engineering, information engineering ,Computer vision ,Segmentation ,image texture ,image segmentation ,DRIVE database images ,Mean curvature ,Blood vessel occlusion ,random walk algorithm ,DRISHTI-GS database images ,lcsh:R855-855.5 ,curve initialisation ,020201 artificial intelligence & image processing ,texture constrained composite weighted random walk algorithm ,MESSIDOR database images ,lcsh:Medical technology ,fundus imaging ,cup-disc ratio-based glaucoma screening ,mean curvature ,optic disc segmentation ,Health Informatics ,deformable model-based OD segmentation techniques ,blood vessel occlusion ,dice coefficient ,Article ,blood vessels ,03 medical and health sciences ,Sørensen–Dice coefficient ,Robustness (computer science) ,cancer ,quantitative performance ,Gabor texture energy features ,business.industry ,edge weights ,OD boundary ,Image segmentation ,Computer Science::Computer Vision and Pattern Recognition ,Artificial intelligence ,neurophysiology ,business ,composite weight function - Abstract
Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.
- Published
- 2018
18. Robust Histopathology Image Analysis: to Label or to Synthesize?
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Ayush Agarwal, Le Hou, Dimitris Samaras, Tahsin Kurc, Rajarsi Gupta, and Joel H. Saltz
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0303 health sciences ,medicine.medical_specialty ,Computer science ,business.industry ,Pipeline (computing) ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Cancer ,Digital pathology ,Pattern recognition ,Image segmentation ,medicine.disease ,Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,ComputingMethodologies_PATTERNRECOGNITION ,Image texture ,medicine ,Segmentation ,Histopathology ,Artificial intelligence ,business ,Importance sampling ,030304 developmental biology - Abstract
Detection, segmentation and classification of nuclei are fundamental analysis operations in digital pathology. Existing state-of-the-art approaches demand extensive amount of supervised training data from pathologists and may still perform poorly in images from unseen tissue types. We propose an unsupervised approach for histopathology image segmentation that synthesizes heterogeneous sets of training image patches, of every tissue type. Although our synthetic patches are not always of high quality, we harness the motley crew of generated samples through a generally applicable importance sampling method. This proposed approach, for the first time, re-weighs the training loss over synthetic data so that the ideal (unbiased) generalization loss over the true data distribution is minimized. This enables us to use a random polygon generator to synthesize approximate cellular structures (i.e., nuclear masks) for which no real examples are given in many tissue types, and hence, GAN-based methods are not suited. In addition, we propose a hybrid synthesis pipeline that utilizes textures in real histopathology patches and GAN models, to tackle heterogeneity in tissue textures. Compared with existing state-of-the-art supervised models, our approach generalizes significantly better on cancer types without training data. Even in cancer types with training data, our approach achieves the same performance without supervision cost. We release code and segmentation results() on over 5000 Whole Slide Images (WSI) in The Cancer Genome Atlas (TCGA) repository, a dataset that would be orders of magnitude larger than what is available today.
- Published
- 2019
19. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment
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Yan Wang, Despina Kontos, Yuanjie Zheng, Shonket Ray, Emily F. Conant, James C. Gee, and Brad M. Keller
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Pathology ,medicine.medical_specialty ,Digital mammography ,Receiver operating characteristic ,medicine.diagnostic_test ,Local binary patterns ,Computer science ,business.industry ,Feature extraction ,Cancer ,Pattern recognition ,General Medicine ,medicine.disease ,Fractal dimension ,Entire breast ,Breast cancer ,Image texture ,Histogram ,Parenchyma ,medicine ,Mammography ,Artificial intelligence ,business - Abstract
Purpose: Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. Methods: Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong’s test was used to compare the different ROCs in terms of AUC performance. Results: The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p 0.05). Conclusions: The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors.
- Published
- 2015
20. Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients
- Author
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Andrew Eugene Hendifar, Nicholas N. Nissen, Howard M. Sandler, Arsen Osipov, Xiao Zhang, Benedick A. Fraass, Yong Yue, and Richard Tuli
- Subjects
Elastic net regularization ,Pathology ,medicine.medical_specialty ,Multivariate statistics ,Multivariate analysis ,PET/CT ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Rare Diseases ,Image texture ,Clinical Research ,Pancreatic cancer ,medicine ,image texture ,Cancer ,PET-CT ,screening and diagnosis ,Framingham Risk Score ,business.industry ,therapy response ,Gastroenterology ,medicine.disease ,Regression ,4.1 Discovery and preclinical testing of markers and technologies ,Detection ,Oncology ,radiomics ,030220 oncology & carcinogenesis ,Biomedical Imaging ,Original Article ,Radiology ,Patient Safety ,business ,Pancreatic adenocarcinoma - Abstract
Background: To stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients. Methods: Twenty-six PA patients treated with RT from 2011–2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis. Results: Median age was 69 y (range, 46–86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation ( Conclusions: Locoregional metabolic texture response provides a feasible approach for evaluating and predicting clinical outcomes following treatment of PA with RT. The proposed method can be used to stratify patient risk and help select appropriate treatment strategies for individual patients toward implementing response-driven adaptive RT.
- Published
- 2017
21. Development of Computer-Aided Diagnostic (CADx) System for Distinguishing Neoplastic from Nonneoplastic Lesions in CT Colonography (CTC): Toward CTC beyond Detection
- Author
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Paul Forti, Abraham H. Dachman, Yonghui Fan, Nazanin Makkinejad, Mohammadamin Zarshenas, Junchi Liu, and Kenji Suzuki
- Subjects
computerised tomography ,medicine.medical_specialty ,subvoxel refinement ,Imaging biomarker ,shape-index-based coarse segmentation ,linear discriminant analysis ,medicine.medical_treatment ,Computed tomography ,colonoscopic polypectomy ,medical image processing ,feature selection ,3D volume growing ,sensitivity analysis ,Biomedical imaging ,CT colonography ,cancer diagnosis ,Medical imaging ,computer-aided diagnostic system ,Colonoscopic Polypectomy ,Medicine ,cancer ,imaging biomarker ,Computer vision ,image texture ,image segmentation ,medicine.diagnostic_test ,Receiver operating characteristic ,morphologic feature analysis ,business.industry ,feature extraction ,neoplastic lesion diagnosis ,area under the receiver operating characteristic curve ,Image segmentation ,Polypectomy ,Colon cancer ,computed tomography images ,machine learning ,texture feature analysis ,Computer-aided ,Wilk lambda-based stepwise feature selection ,Lesions ,Three-dimensional displays ,computer-aided diagnosis ,Artificial intelligence ,Radiology ,business - Abstract
Half of the polyps surgically removed during conventional colonoscopy are benign with no malignant potential. Our purpose was to develop a CADx system for distinction between neoplastic and non-neoplastic lesions in CTC to reduce "unnecessary" colonoscopic polypectomy. Although computer aided detection (CADe) systems have been developed, less attention was given to the development of CADx systems. Our CADx system consists of shape-index-based coarse segmentation of lesions, 3D volume growing and sub-voxel refinement for fine segmentation of lesions, morphologic and texture feature analysis, Wilks' lambda-based stepwise feature selection, linear discriminant analysis for providing an integrated imaging biomarker for diagnosis of neoplastic lesions. Our database contained biopsy-confirmed 54 neoplastic lesions in 29 patients and 14 non-neoplastic lesions in 10 patients. Our CADx system integrating the selected features was able to determine an accurate likelihood of being a neoplasm and distinguish 87% (47/54) neoplastic lesions from 57% (8/14) non-neoplastic lesions correctly only using computed tomography (CT) images, achieving an area under the receiver operating characteristic curve (AUC) of 0.82. This study showed the potential of the use of CTC as a diagnostic tool beyond already accepted detection, thus, CTC with CADx would be potentially useful for reducing "unnecessary" polypectomy.
- Published
- 2015
22. Quantitative analysis of breast echotexture patterns in automated breast ultrasound images
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Min Sun Bae, Yu Ling Hou, Woo Kyung Moon, Jeon-Hor Chen, Chung Ming Lo, Jung Min Chang, Won Hwa Kim, Ruey-Feng Chang, and Chiun-Sheng Huang
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Pathology ,medicine.medical_specialty ,medicine.diagnostic_test ,Receiver operating characteristic ,Contextual image classification ,business.industry ,Computer science ,Feature extraction ,MAMMOGRAPHIC DENSITY ,Cancer ,Pattern recognition ,General Medicine ,medicine.disease ,Breast cancer ,Image texture ,Histogram ,medicine ,Mammography ,Artificial intelligence ,Ultrasonography ,skin and connective tissue diseases ,business ,Breast ultrasound - Abstract
Purpose: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using automated breast ultrasound (ABUS) images. Methods: A CAC system was proposed that can recognize breast echotexture patterns in ABUS images. For each case, the echotexture pattern was assessed by two expert radiologists and classified as heterogeneous or homogeneous. After neutrosophic image transformation and fuzzy c-mean clusterings, the lower and upper boundaries of the fibroglandular tissues were defined. Then, the number of hypoechoic regions and histogram features were extracted from the fibroglandular tissues, and the support vector machine model with the leave-one-out cross-validation method was utilized as the classifier. The authors’ database included a total of 208 ABUS images of the breasts of 104 females. Results: The accuracies of the proposed system for the classification of heterogeneous and homogeneous echotexture patterns were 93.48% (43/46) and 92.59% (150/162), respectively, with an overall Az (area under the receiver operating characteristic curve) of 0.9786. The agreement between the radiologists and the proposed system was almost perfect, with a kappa value of 0.814. Conclusions: The use of ABUS and the proposed method can provide quantitative information on the echotexture patterns of the breast and can be used to evaluate whether breast echotexture patterns are associated with breast cancer risk in the future.
- Published
- 2015
23. Voxel-level reproducibility assessment of modality independent elastography in a pre-clinical murine model
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Jared A. Weis, Katelyn Flint, Michael I. Miga, and Thomas E. Yankeelov
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Reproducibility ,medicine.medical_specialty ,medicine.diagnostic_test ,business.industry ,Cancer ,computer.software_genre ,medicine.disease ,Imaging phantom ,Magnetic resonance elastography ,Breast cancer ,Image texture ,Voxel ,Murine model ,Tissue elasticity ,medicine ,Medical physics ,Elastography ,business ,computer ,Biomedical engineering - Abstract
Changes in tissue mechanical properties, measured non-invasively by elastography methods, have been shown to be an important diagnostic tool, particularly for cancer. Tissue elasticity information, tracked over the course of therapy, may be an important prognostic indicator of tumor response to treatment. While many elastography techniques exist, this work reports on the use of a novel form of elastography that uses image texture to reconstruct elastic property distributions in tissue (i.e., a modality independent elastography (MIE) method) within the context of a pre-clinical breast cancer system.1,2 The elasticity results have previously shown good correlation with independent mechanical testing.1 Furthermore, MIE has been successfully utilized to localize and characterize lesions in both phantom experiments and simulation experiments with clinical data.2,3 However, the reproducibility of this method has not been characterized in previous work. The goal of this study is to evaluate voxel-level reproducibility of MIE in a pre-clinical model of breast cancer. Bland-Altman analysis of co-registered repeat MIE scans in this preliminary study showed a reproducibility index of 24.7% (scaled to a percent of maximum stiffness) at the voxel level. As opposed to many reports in the magnetic resonance elastography (MRE) literature that speak to reproducibility measures of the bulk organ, these results establish MIE reproducibility at the voxel level; i.e., the reproducibility of locally-defined mechanical property measurements throughout the tumor volume.
- Published
- 2015
24. Detection of melanoma using distinct features
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K. N. Nagesh, M N Giriprasad, D. Satyanarayana, and T Y Satheesha
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business.industry ,Computer science ,Melanoma ,Feature extraction ,3D reconstruction ,Scale-space segmentation ,Cancer ,Pattern recognition ,02 engineering and technology ,Image segmentation ,021001 nanoscience & nanotechnology ,medicine.disease ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Image texture ,Initial phase ,medicine ,RGB color model ,Computer vision ,Artificial intelligence ,0210 nano-technology ,business ,Skin lesion - Abstract
Melanoma is one of the deadliest cancer to be treated as well to detect in initial phase. Here we take the skin lesion by ROI and then we take out features of it then it needs to be segmented whether the particular image is cancerous or not. If it is cancerous then classify the extracted features and discuss about type of stages. A novel 3D reconstruction algorithm from 2D dermoscopic images is proposed. The detection of 3D image shape and RGB are to be carried out. In this paper we have proposed this work for 3D depth parameter which will enhance the classification rate.
- Published
- 2016
25. Curvelet-based classification of prostate cancer histological images of critical Gleason scores
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Christhunesa S. Christudass, Robert W. Veltri, Wen-Chyi Lin, Ching-Chung Li, and Jonathan I. Epstein
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Tissue microarray ,Contextual image classification ,Computer science ,business.industry ,Prostatectomy ,medicine.medical_treatment ,Feature extraction ,Cancer ,Pattern recognition ,medicine.disease ,Support vector machine ,Prostate cancer ,Image texture ,medicine ,Curvelet ,Computer vision ,Artificial intelligence ,business ,Classifier (UML) - Abstract
This paper is aimed at the development of an approach of applying the curvelet transform to images of prostatectomy pathological specimens of critical Gleason grades for computer-aided classification. A set of Tissue MicroArray (TMA) images from the Johns Hopkins University have been used as the data base. We utilize a moving window to sample multiple patches of a given image leading to a majority decision by the patches for image class assignment. The curvelet-based feature extraction may capture both textural and, implicitly, structural information in an image patch. A tree-structured classifier consisting of three Gaussian-kernel support vector machines each with an embedded voting mechanism has been successfully trained and tested yielding high accuracy to classify tissue images of four critical Gleason scores (GS) 3+3, 3+4, 4+3 and 4+4. The experimental result has demonstrated an enhanced performance as compared to other reported works.
- Published
- 2015
26. Researcher from University of Science and Technology Reports Recent Findings in Cancer (Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes).
- Subjects
SCIENCE journalism ,BIOMARKERS ,GENES ,NON-small-cell lung carcinoma ,GENE regulatory networks - Abstract
Keywords: Algorithms; Biomarkers; Cancer; Diagnostics and Screening; Genetics; Health and Medicine; Oncology EN Algorithms Biomarkers Cancer Diagnostics and Screening Genetics Health and Medicine Oncology Researchers detail new data in cancer. Algorithms, Biomarkers, Cancer, Diagnostics and Screening, Genetics, Health and Medicine, Oncology. [Extracted from the article]
- Published
- 2023
27. Increased DNA Damage and Insufficient DNA Repair in Euthyroid Patients With Nodular Goiter Analyzed by γ-H2AX and 53BP1 Foci Assay.
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Duzgun, Busra, Bayram, Fahri, Korkmaz-Bayram, Keziban, Hamurcu, Zuhal, and Donmez-Altuntas, Hamiyet
- Subjects
THYROID nodules ,DNA repair ,DNA damage ,PHOSPHORYLATION ,BIOMARKERS - Abstract
Objective: Thyroid nodules are a common occurrence in adults. Although the majority of thyroid nodules are benign, a small percentage are cancerous. The combined phosphorylated histone H2AX (γ-H2AX) and p53-binding protein 1 (53BP1) assay was utilized to detect deoxyribonucleic acid (DNA) damage and DNA repair as biomarkers of the cellular stress response. Using the combined γ-H2AX and 53BP1 assay, we evaluated DNA damage, DNA repair capacity, and malignancy risk in peripheral blood mononuclear cells (PBMCs) of euthyroid individuals with nodular goiter. Materials and Methods: Peripheral blood samples were collected from 33 euthyroid patients with newly diagnosed nodular goiter and 30 healthy control participants. A fully automatic image analysis system was used for analyzing DNA damage (γ-H2AX), including DNA double-strand breaks (DSBs), and DNA repair (53BP1). Results: Euthyroid patients with nodular goiter exhibited a higher mean number of γ-H2AX foci per cell and a higher percentage of apoptotic cells compared to the control subjects (p=0.022 and p=0.005, respectively). Conclusion: This study found considerably higher DNA damage in euthyroid patients with nodular goiter than in control individuals. The increase in DNA damage occurs in the early stages of carcinogenesis. These patients were expected to exhibit compromised DNA repair along with enhanced DNA damage, increasing the risk of carcinogenesis. However, euthyroid patients with nodular goiter might be at a high risk of thyroid malignancy due to the high level of DNA damage. A long-term follow-up of these patients would provide better evidence of the relationship between DNA damage and the malignancy risk of thyroid. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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28. Automatic anatomy recognition in whole-body PET/CT images.
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Wang, Huiqian, Udupa, Jayaram K., Odhner, Dewey, Tong, Yubing, Zhao, Liming, and Torigian, Drew A.
- Subjects
- *
OBJECT recognition (Computer vision) , *POSITRON emission tomography , *COMPUTED tomography , *WHOLE body imaging systems (Security screening) , *FUZZY logic , *TEXTURE analysis (Image processing) - Abstract
Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18, 752-771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-regionwise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
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29. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.
- Author
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Zheng, Yuanjie, Keller, Brad M., Ray, Shonket, Wang, Yan, Conant, Emily F., Gee, James C., and Kontos, Despina
- Subjects
- *
DIGITAL mammography , *BREAST cancer risk factors , *FEATURE extraction , *LOGISTIC regression analysis , *FRACTAL dimensions - Abstract
Purpose: Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. Methods: Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance. Results: The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). Conclusions: The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
30. Quantitative Ultrasound for Evaluation of Tumour Response to Ultrasound-Microbubbles and Hyperthermia.
- Author
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Sharma, Deepa, Carter, Holliday, Sannachi, Lakshmanan, Cui, Wentao, Giles, Anoja, Saifuddin, Murtuza, and Czarnota, Gregory J.
- Subjects
BREAST ,FEVER ,ULTRASONIC imaging ,HINDLIMB ,TUMORS ,CELL nuclei - Abstract
Objectives: Prior study has demonstrated the implementation of quantitative ultrasound (QUS) for determining the therapy response in breast tumour patients. Several QUS parameters quantified from the tumour region showed a significant correlation with the patient's clinical and pathological response. In this study, we aim to identify if there exists such a link between QUS parameters and changes in tumour morphology due to combined ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) using the breast xenograft model (MDA-MB-231). Method: Tumours grown in the hind leg of severe combined immuno-deficient mice were treated with permutations of USMB and HT. Ultrasound radiofrequency data were collected using a 25 MHz array transducer, from breast tumour-bearing mice prior and post-24-hour treatment. Result: Our result demonstrated an increase in the QUS parameters the mid-band fit and spectral 0-MHz intercept with an increase in HT duration combined with USMB which was found to be reflective of tissue structural changes and cell death detected using haematoxylin and eosin and terminal deoxynucleotidyl transferase dUTP nick end labelling stain. A significant decrease in QUS spectral parameters was observed at an HT duration of 60 minutes, which is possibly due to loss of nuclei by the majority of cells as confirmed using histology analysis. Morphological alterations within the tumour might have contributed to the decrease in backscatter parameters. Conclusion: The work here uses the QUS technique to assess the efficacy of cancer therapy and demonstrates that the changes in ultrasound backscatters mirrored changes in tissue morphology. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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31. Identification of genomic biomarkers and their pathway crosstalks for deciphering mechanistic links in glioblastoma.
- Author
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Quddusi, Darrak Moin and Bajcinca, Naim
- Subjects
TYPE I interferons ,GLIOBLASTOMA multiforme ,HUNTINGTON disease ,CIRCULATING tumor DNA ,GENETIC translation ,BIOMARKERS ,BLOOD platelets ,INTERFERON receptors - Abstract
Glioblastoma is a grade IV pernicious neoplasm occurring in the supratentorial region of brain. As its causes are largely unknown, it is essential to understand its dynamics at the molecular level. This necessitates the identification of better diagnostic and prognostic molecular candidates. Blood‐based liquid biopsies are emerging as a novel tool for cancer biomarker discovery, guiding the treatment and improving its early detection based on their tumour origin. There exist previous studies focusing on the identification of tumour‐based biomarkers for glioblastoma. However, these biomarkers inadequately represent the underlying pathological state and incompletely illustrate the tumour because of non‐recursive nature of this approach to monitor the disease. Also, contrary to the tumour biopsies, liquid biopsies are non‐invasive and can be performed at any interval during the disease span to surveil the disease. Therefore, in this study, a unique dataset of blood‐based liquid biopsies obtained primarily from tumour‐educated blood platelets (TEP) is utilised. This RNA‐seq data from ArrayExpress is acquired comprising human cohort with 39 glioblastoma subjects and 43 healthy subjects. Canonical and machine learning approaches are applied for identification of the genomic biomarkers for glioblastoma and their crosstalks. In our study, 97 genes appeared enriched in 7 oncogenic pathways (RAF‐MAPK, P53, PRC2‐EZH2, YAP conserved, MEK‐MAPK, ErbB2 and STK33 signalling pathways) using GSEA, out of which 17 have been identified participating actively in crosstalks. Using PCA, 42 genes are found enriched in 7 pathways (cytoplasmic ribosomal proteins, translation factors, electron transport chain, ribosome, Huntington's disease, primary immunodeficiency pathways, and interferon type I signalling pathway) harbouring tumour when altered, out of which 25 actively participate in crosstalks. All the 14 pathways foster well‐known cancer hallmarks and the identified DEGs can serve as genomic biomarkers, not only for the diagnosis and prognosis of Glioblastoma but also in providing a molecular foothold for oncogenic decision making in order to fathom the disease dynamics. Moreover, SNP analysis for the identified DEGs is performed to investigate their roles in disease dynamics in an elaborated manner. These results suggest that TEPs are capable of providing disease insights just like tumour cells with an advantage of being extracted anytime during the course of disease in order to monitor it. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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32. A Bayesian nonparametric model for textural pattern heterogeneity
- Author
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Li, Xiao, Guindani, Michele, Ng, Chaan S, and Hobbs, Brian P
- Subjects
Cancer ,Biomedical Imaging ,Bayesian nonparametrics ,cancer radiomics ,grey‐ ,level co‐ ,occurrence matrix ,multivariate count data ,stat.AP ,Statistics ,Statistics & Probability - Abstract
Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumour heterogeneity through patterns of enhancement, texture, morphology and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of grey-level co-occurrence matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine learning approaches currently used in cancer radiomics with simulation studies.
- Published
- 2021
33. Artificial Intelligence in CT and MR Imaging for Oncological Applications.
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Paudyal, Ramesh, Shah, Akash D., Akin, Oguz, Do, Richard K. G., Konar, Amaresha Shridhar, Hatzoglou, Vaios, Mahmood, Usman, Lee, Nancy, Wong, Richard J., Banerjee, Suchandrima, Shin, Jaemin, Veeraraghavan, Harini, and Shukla-Dave, Amita
- Subjects
HEAD & neck cancer diagnosis ,DEEP learning ,ARTIFICIAL intelligence ,MAGNETIC resonance imaging ,LUNG tumors ,ABDOMINAL tumors ,PELVIC tumors ,COMPUTED tomography ,DECISION making in clinical medicine ,ONCOLOGY - Abstract
Simple Summary: The two most common cross-sectional imaging modalities, computed tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in clinical oncology. The emergence of artificial intelligence (AI)-based tools in medical imaging has been motivated by the desire for greater efficiency and efficacy in clinical care. Although a growing number of new AI tools for narrow-specific tasks in imaging is highly encouraging, the effort to tackle the key challenges to implementation by the worldwide imaging community has yet to be appropriately addressed. In this review, we discuss a few challenges in using AI tools and offer some potential solutions with examples from lung CT and MRI of the abdomen, pelvis, and head and neck (HN) region. As we advance, AI tools may significantly enhance clinician workflows and clinical decision-making. Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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34. Extracting Morphological and Sub-Resolution Features from Optical Coherence Tomography Images, a Review with Applications in Cancer Diagnosis.
- Author
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Photiou, Christos, Kassinopoulos, Michalis, and Pitris, Costas
- Subjects
DIAGNOSTIC imaging ,OPTICAL coherence tomography ,CANCER diagnosis ,FEATURE extraction ,REFRACTIVE index ,THERAPEUTICS - Abstract
Before they become invasive, early cancer cells exhibit specific and characteristic changes that are routinely used by a histopathologist for diagnosis. Currently, these early abnormalities are only detectable ex vivo by histopathology or, non-invasively and in vivo, by optical modalities that have not been clinically implemented due to their complexity and their limited penetration in tissues. Optical coherence tomography (OCT) is a noninvasive medical imaging technology with increasing clinical applications in areas such as ophthalmology, cardiology, gastroenterology, etc. In addition to imaging the tissue micro-structure, OCT can also provide additional information, describing the constituents and state of the cellular components of the tissue. Estimates of the nuclear size, sub-cellular morphological variations, dispersion and index of refraction can be extracted from the OCT images and can serve as diagnostically useful biomarkers. Moreover, the development of fully automated algorithms for tissue segmentation and feature extraction and the application of machine learning, can further enhance the clinical potential of OCT. When fully exploited, OCT has the potential to lead to accurate and sensitive, image-derived, biomarkers for disease diagnosis and treatment monitoring of cancer. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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35. Deep Learning With Radiogenomics Towards Personalized Management of Gliomas.
- Author
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Mitra, Sushmita
- Abstract
A state-of-the-art interdisciplinary survey on multi-modal radiogenomic approaches is presented involving applications to the diagnosis and personalized management of gliomas a common kind of brain tumors through noninvasive imaging integrated with genomic information. It encompasses mining tumor radioimages employing deep learning for the automated extraction of relevant features from the segmented volume of interest (VOI). Gene expression values from surgically extracted tumor tissues are often simultaneously analyzed to determine patient specific features. Association between genomic and radiomic features are also explored in some cases to determine the imaging surrogates. Deep learning and transfer learning are typically exploited for efficient knowledge discovery and decision-making. Some studies on survival prediction ensemble learning and interactive learning are also included. The literature mainly focuses on magnetic resonance imaging (MRI) data of the brain for learning and validation and generally involves the NIH TCIA and TCGA repositories as well as the BraTS Challenge databases. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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36. Tumor radiomic features complement clinico-radiological factors in predicting long-term local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancers.
- Author
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Agarwal, Jai Prakash, Sinha, Shwetabh, Goda, Jayant Sastri, Joshi, Kishor, Mhatre, Ritesh, Kannan, Sadhana, Laskar, Sarbani Ghosh, Gupta, Tejpal, Murthy, Vedang, Budrukkar, Ashwini, Mummudi, Naveen, and Ganeshan, Balaji
- Subjects
HISTOGRAMS ,PROGRESSION-free survival ,SQUAMOUS cell carcinoma ,SPATIAL filters ,MULTIVARIATE analysis ,STANDARD deviations ,CANCER - Abstract
To study if pre-treatment CT texture features in locally advanced squamous cell carcinoma of laryngo-pharynx can predict long-term local control and laryngectomy free survival (LFS). Image texture features of 60 patients treated with chemoradiation (CTRT) within an ethically approved study were studied on contrast-enhanced images using a texture analysis research software (TexRad, UK). A filtration-histogram technique was used where the filtration step extracted and enhanced features of different sizes and intensity variations corresponding to a particular spatial scale filter (SSF): SSF = 0 (without filtration), SSF = 2 mm (fine texture), SSF = 3–5 mm (medium texture) and SSF = 6 mm (coarse texture). Quantification by statistical and histogram technique comprised mean intensity, standard-deviation, entropy, mean positive pixels, skewness and kurtosis. The ability of texture analysis to predict LFS or local control was determined using Kaplan–Meier analysis and multivariate cox model. Median follow-up of patients was 24 months (95% CI:20–28). 39 (65%) patients were locally controlled at last follow-up. 10 (16%) had undergone salvage laryngectomy after CTRT. For both local control & LFS, threshold optimal cut-off values of texture features were analyzed. Medium filtered-texture feature that were associated with poorer laryngectomy free survival were entropy ≥4.54, (p = 0.006), kurtosis ≥4.18; p = 0.019, skewness ≤−0.59, p = 0.001, and standard deviation ≥43.18; p = 0.009). Inferior local control was associated with medium filtered features entropy ≥4.54; p 0.01 and skewness ≤ – 0.12; p = 0.02. Using fine filters, entropy ≥4.29 and kurtosis ≥−0.27 were also associated with inferior local control (p = 0.01 for both parameters). Multivariate analysis showed medium filter entropy as an independent predictor for LFS and local control (p < 0.001 & p = 0.001). Medium texture entropy is a predictor for inferior local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancer and this can complement clinico-radiological factors in predicting prognosticating these tumors. Texture features play an important role as a surrogate imaging biomarker for predicting local control and laryngectomy free survival in locally advanced laryngo-pharyngeal tumors treated with definitive chemoradiation. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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37. A review of radiomics and genomics applications in cancers: the way towards precision medicine.
- Author
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Li, Simin and Zhou, Baosen
- Subjects
RADIOMICS ,INDIVIDUALIZED medicine ,GENOMICS ,NUCLEOTIDE sequencing ,DIGITAL images - Abstract
The application of radiogenomics in oncology has great prospects in precision medicine. Radiogenomics combines large volumes of radiomic features from medical digital images, genetic data from high-throughput sequencing, and clinical-epidemiological data into mathematical modelling. The amalgamation of radiomics and genomics provides an approach to better study the molecular mechanism of tumour pathogenesis, as well as new evidence-supporting strategies to identify the characteristics of cancer patients, make clinical decisions by predicting prognosis, and improve the development of individualized treatment guidance. In this review, we summarized recent research on radiogenomics applications in solid cancers and presented the challenges impeding the adoption of radiomics in clinical practice. More standard guidelines are required to normalize radiomics into reproducible and convincible analyses and develop it as a mature field. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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38. RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment.
- Author
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Antunes, Jacob T., Ismail, Marwa, Hossain, Imran, Wang, Zhoumengdi, Prasanna, Prateek, Madabhushi, Anant, Tiwari, Pallavi, and Viswanath, Satish E.
- Subjects
TUMOR treatment ,GLIOBLASTOMA multiforme ,RADIOMICS ,HETEROGENEITY ,TEXTURE analysis (Image processing) ,PROGNOSIS ,ENDORECTAL ultrasonography - Abstract
Localized disease heterogeneity on imaging extracted via radiomics approaches have recently been associated with disease prognosis and treatment response. Traditionally, radiomics analyses leverage texture operators to derive voxel- or region-wise feature values towards quantifying subtle variations in image appearance within a region-of-interest (ROI). With the goal of mining additional voxel-wise texture patterns from radiomic “expression maps”, we introduce a new RADIomic Spatial TexturAl descripTor (RADISTAT). This was driven by the hypothesis that quantifying spatial organization of texture patterns within an ROI could allow for better capturing interactions between different tissue classes present in a given region; thus enabling more accurate characterization of disease or response phenotypes. RADISTAT involves: (a) robustly identifying sub-compartments of low, intermediate, and high radiomic expression (i.e. heterogeneity) in a feature map and (b) quantifying spatial organization of sub-compartments via graph interactions. RADISTAT was evaluated in two clinically challenging problems: (1) discriminating nodal/distant metastasis from metastasis-free rectal cancer patients on post-chemoradiation T2w MRI, and (2) distinguishing tumor progression from pseudo-progression in glioblastoma multiforme using post-chemoradiation T1w MRI. Across over 800 experiments, RADISTAT yielded a consistent discriminatory signature for tumor progression (GBM) and disease metastasis (RCa); where its sub-compartments were associated with pathologic tissue types (fibrosis or tumor, determined via fusion of MRI and pathology). In a multi-institutional setting for both clinical problems, RADISTAT resulted in higher classifier performance (11% improvement in AUC, on average) compared to radiomic descriptors. Furthermore, combining RADISTAT with radiomic descriptors resulted in significantly improved performance compared to using radiomic descriptors alone. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
39. Development Of F18-Fdg Pet/Ct Database Of Lung Masses And Computation Of Their Feature Vector.
- Subjects
NON-small-cell lung carcinoma ,LUNG cancer ,HEMOPTYSIS ,COUGH ,CHEST pain ,CANCER ,NUCLEAR medicine - Abstract
Cancer is a generic term for a large group of diseases that can affect any part of the body. Lungs cancer is one of the most common and serious type of cancer. Lung cancer is the leading cause of cancer death worldwide [1]. In US alone, the estimated new lung cancer cases for 2017 are 222, 500 out of which 116, 990 are males and 105, 510 females. Estimated deaths are 155, 870 out of which 84, 590 are male and 71, 280 are female [2]. In India, the estimated cases of men and women in 2012 were 54000 & 17000 respectively [1]. Lungs cancer is a malignant lungs tumor characterized by uncontrolled cell growth in tissues of the lung. If it is left untreated, this growth can spread beyond the lungs by process of metastasis into nearby tissues or other parts of the body [3]. There are mainly two types of lungs cancer small-cell lungs carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC). NSCLC also categorized into three parts- Adenocarcinoma, Squamous cell carcinoma and Large cell carcinoma. The most common symptoms are coughing up blood, weight loss, shortness of breath, cough that does not go away, coughing up blood, fatigue, losing weight without trying, loss of appetite, shortness of breath and wheezing and chest pain. The vast majority (85%) of cases of lungs cancer are due to long -term tobacco smoking. About 10-15% of cases occur in people who have never smoked but it happened due to genetic factors, exposure to radon gas, asbestos, second hand smoke or other form of air pollution. Lungs cancer may be seen on chest radiographs and computed tomography but diagnosis confirmed by biopsy which is usually performed by bronchoscope or CT or by PET/CT guidance. F18-FDG PET/CT scanning is best for all types of cancers because of better sensitivity and specificity compared to anatomical imaging such as CT as it provides estimates of tumor glucose metabolism [4]. Standardized uptake value (SUV) is the semi-quantitative parameter which can be estimated from F18-FDG PET studies and routinely used for characterizing of the tumor and assessment of treatment response evaluation in these patients. The cut off value of SUV is 3.5 above which tumor is malignant otherwise benign [5]. However, there are many variables such as amount of activity injected, blood glucose level, time of injection and weight of patient, which can affect the estimation of SUV. Hence, majority of nuclear medicine physician rely more on their visual assessment and use it for reporting the F18-FDG PET/CT Scan. Now, the nuclear medicine community is looking for another reliable quantitative parameters extracted from the image that will be used in diagnosis and/or treatment response evaluation. Image processing algorithms have potential to assist in lesion (e.g. nodule) detection on PET/CT studies and to assess the stability or change in size of lesion on serial PET/CT studies. Comparison and evaluation of image processing techniques against each other require common data sets and standardized methods for evaluation. Investigators developing image processing algorithms need standardized databases with which to work. Therefore, there is a need for F18-FDG PET/CT image database as research resource for medical image processing. PET image texture analysis was proposed to characterize the heterogeneity of tumor F18-FDG uptake [6-9, 37]. F18-FDG uptake is not homogeneous across the tumor because of necrosis, cell proliferation, micro vessel density, and hypoxia [6, 10-12]. It has been shown that tumor heterogeneity can be associated with disease progression, response to therapy, and malignant behavior of the tumor [6, 13]. Texture analysis refers to a variety of mathematical methods that may be applied to describe the relationship between the grey level intensity of pixels or voxels and their position within an image. An advantage of measuring textural parameters is that it is a post processing technique that can be applied to data acquired during standard clinical imaging protocols thereby maximizing the information that can be derived from standard clinical images. A number of textural features can be derived that provide a measure of intralesional heterogeneity e.g. angular second moment, inverse difference moment, entropy, correlation etc. [6, 14, 26]. Texture analysis has been extensively used in CT images and has given promising results as a predictor of survival and in treatment response assessment in NSCLC and other carcinomas [15-18, 27], but it is still new and emerging field in PET/CT. Very few studies has been found in the literature in which texture analysis is used in F18-FDG PET/CT scans to predict patient outcome and treatment response in oncology[19-22, 34-36]. However, only one study has been found in literature on the prediction of treatment response of NSCLC using texture analysis in F18-FDG PET/CT scans [22]. Local Binary Pattern (LBP) is a method of texture analysis it is based on small area. It is based on the texture spectrum model and provides an additional statistical approach to texture analysis. In texture spectrum model a concept of texture unit is proposed. The texture unit is defined for each pixel value by the eight neighboring pixels values in a 3x3 matrix. Each neighboring pixel is compared to the central pixel and a texture unit value is assigned accordingly. Neighboring intensities with threshold lower values compared to the reference pixel are marked with 0, intensity values equal or greater than the reference pixel are marked with 1. The texture unit is read in starting from upper left corner of the newly calculated 3x3 matrix proceeding clockwise. The intent of this study is to construct a database of F18-FDG PET/ CT images of lung masses. [ABSTRACT FROM AUTHOR]
- Published
- 2017
40. Computer-assisted grading of follicular lymphoma: a classification based on SVM, machine learning, and transfer learning approaches.
- Author
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Saxena, Pranshu and Goyal, Anjali
- Subjects
FOLLICULAR lymphoma ,MACHINE learning ,DEEP learning ,FEATURE extraction ,VECTOR spaces ,K-means clustering - Abstract
This proposed work implements various classification approaches to classify the H&E-stained Follicular Lymphoma tissue sample. As part of the process, k-means clustering is used to isolate cytological components like the nucleus extracellular and cytoplasmic regions from images. Then feature space vector is constructed by combining global feature extraction techniques like LBP, LDP, GLCM, and other local feature-extraction techniques. To classify FL images into their respective grades, feature space vectors obtained from feature extraction algorithms are input into a multiclass SVM. Moreover, classification accuracies were explicitly tested with different classifiers like CNN and other pre-trained deep learning networks that can directly operate on raw images without any preprocessing to classify the FL images into their respective grades. The efficacy of different classifiers is presented. This preliminary work provides a proof of concept for incorporating automated FL tissue diagnostic systems into future pathology workflows to supplement the pathologists'. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
41. Computed tomography-based radiomics approach in pancreatic tumors characterization.
- Author
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Karmazanovsky, Grigory, Gruzdev, Ivan, Tikhonova, Valeriya, Kondratyev, Evgeny, and Revishvili, Amiran
- Abstract
Radiomics (or texture analysis) is a new imaging analysis technique that allows calculating the distribution of texture features of pixel and voxel values depend on the type of ROI (3D or 2D), their relationships in the image. Depending on the software, up to several thousand texture elements can be obtained. Radiomics opens up wide opportunities for differential diagnosis and prognosis of pancreatic neoplasias. The aim of this review was to highlight the main diagnostic advantages of texture analysis in different pancreatic tumors. The review describes the diagnostic performance of radiomics in different pancreatic tumor types, application methods, and problems. Texture analysis in PDAC is able to predict tumor grade and associates with lymphovascular invasion and postoperative margin status. In pancreatic neuroendocrine tumors, texture features strongly correlate with differentiation grade and allows distinguishing it from the intrapancreatic accessory spleen. In pancreatic cystic lesions, radiomics is able to accurately differentiate MCN from SCN and distinguish clinically insignificant lesions from IPMNs with advanced neoplasia. In conclusion, the use of the CT radiomics approach provides a higher diagnostic performance of CT imaging in pancreatic tumors differentiation and prognosis. Future studies should be carried out to improve accuracy and facilitate radiomics workflow in pancreatic imaging. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
42. CacheTrack-YOLO: Real-Time Detection and Tracking for Thyroid Nodules and Surrounding Tissues in Ultrasound Videos.
- Author
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Wu, Xiangqiong, Tan, Guanghua, Zhu, Ningbo, Chen, Zhilun, Yang, Yan, Wen, Huaxuan, and Li, Kenli
- Subjects
THYROID nodules ,ULTRASONIC imaging ,COMPUTER-aided diagnosis ,COMPUTER-assisted image analysis (Medicine) ,VIDEO surveillance ,VIDEOS ,THYROID gland ,DIAGNOSTIC ultrasonic imaging - Abstract
To accurately detect and track the thyroid nodules in a video is a crucial step in the thyroid screening for identification of benign and malignant nodules in computer-aided diagnosis (CAD) systems. Most existing methods just perform excellent on static frames selected manually from ultrasound videos. However, manual acquisition is labor-intensive work. To make the thyroid screening process in a more natural way with less labor operations, we develop a well-designed framework suitable for practical applications for thyroid nodule detection in ultrasound videos. Particularly, in order to make full use of the characteristics of thyroid videos, we propose a novel post-processing approach, called Cache-Track, which exploits the contextual relation among video frames to propagate the detection results into adjacent frames to refine the detection results. Additionally, our method can not only detect and count thyroid nodules, but also track and monitor surrounding tissues, which can greatly reduce the labor work and achieve computer-aided diagnosis. Experimental results show that our method performs better in balancing accuracy and speed. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
43. Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification.
- Author
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Heidari, Morteza, Lakshmivarahan, Sivaramakrishnan, Mirniaharikandehei, Seyedehnafiseh, Danala, Gopichandh, Maryada, Sai Kiran R., Liu, Hong, and Zheng, Bin
- Subjects
COMPUTER-aided diagnosis ,MACHINE learning ,BREAST ,SUPPORT vector machines ,COMPUTER-assisted image analysis (Medicine) ,ALGORITHMS - Abstract
Objective: Since computer-aided diagnosis (CAD) schemes of medical images usually computes large number of image features, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models, the objective of this study is to investigate feasibility of applying a random projection algorithm (RPA) to build an optimal feature vector from the initially CAD-generated large feature pool and improve performance of machine learning model. Methods: We assemble a retrospective dataset involving 1,487 cases of mammograms in which 644 cases have confirmed malignant mass lesions and 843 have benign lesions. A CAD scheme is first applied to segment mass regions and initially compute 181 features. Then, support vector machine (SVM) models embedded with several feature dimensionality reduction methods are built to predict likelihood of lesions being malignant. All SVM models are trained and tested using a leave-one-case-out cross-validation method. SVM generates a likelihood score of each segmented mass region depicting on one-view mammogram. By fusion of two scores of the same mass depicting on two-view mammograms, a case-based likelihood score is also evaluated. Results: Comparing with the principle component analyses, nonnegative matrix factorization, and Chi-squared methods, SVM embedded with RPA yielded a significantly higher case-based lesion classification performance with the area under ROC curve of 0.84 ± 0.01 (p<0.02). Conclusion: The study demonstrates that RPA is a promising method to generate optimal feature vectors and improve SVM performance. Significance: This study presents a new method to develop CAD schemes with significantly higher and robust performance. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
44. Particle swarm optimization based segmentation of Cancer in multi-parametric prostate MRI.
- Author
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Garg, Gaurav and Juneja, Mamta
- Subjects
COMPUTER-aided diagnosis ,PARTICLE swarm optimization ,PROSTATE cancer ,MAGNETIC resonance imaging ,MEDICAL personnel ,ENDORECTAL ultrasonography - Abstract
Prostate Cancer (PCa) is one among the prominent causes of mortality in men, which can only be reduced by early diagnosis. Multi-parametric Magnetic Resonance Imaging (mp-MRI) is increasingly utilized by clinicians for performing diagnostics tasks because it possesses functional and morphological competencies. Although, manual segmentation of PCa on MRI is a tedious, operator-dependent and time consuming task. Therefore, Computer Aided Diagnosis (CAD) of PCa using mp-MRI images is highly desirable by employing computer-assisted segmentation approaches. In this paper, a method is proposed for segmentation of PCa based on level set with Particle Swarm Optimization (PSO) technique to address the limitations of existing techniques as PSO does not require any cost or objective function to be differentiable and it is easy to implement. The energy function is optimized with PSO based technique. The proposed approach is tested over three different mp-MRI modalities i.e., T2 weighted (T2w), Dynamic Contrast Enhanced (DCE) images and Apparent Diffusion Coefficient (ADC) Maps derived from Diffusion Weighted Images (DWI). The accuracy achieved by PSO based methodology is 7.6% greater than without PSO integration i.e., using Gradient descent with added computational overhead of 0.03 s. The experimental outcomes reveal that the proposed methodology shows better results in terms of considered evaluation metrics when compared with the existing techniques on the I2CVB dataset. The impact of the proposed methodology is that it has the ability for precise segmentation even with intensity inhomogeneity, which validates its applications in clinical treatments. Additionally, the proposed technique reduces the manual interference, which in turn minimizes the execution time. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
45. Efficient computer‐aided diagnosis technique for leukaemia cancer detection.
- Author
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Abdulla, Alan Anwer
- Abstract
Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
46. Social bat optimisation dependent deep stacked auto‐encoder for skin cancer detection.
- Author
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Majji, Ramachandro, Om Prakash, Ponnusamy Gnanaprakasam, Cristin, Rajan, and Parthasarathy, Govindaswamy
- Abstract
Nowadays, skin cancer is one of the most dangerous forms of cancer found in humans. There are various types of skin cancer, like basal, melanoma, carcinoma, and the squamous cell from which the melanoma is unpredictable. Thus, skin cancer detection in the early stage is very useful to treat it successfully. Hence, this study introduces a new algorithm called social bat optimisation algorithm for skin cancer detection. Initially, the pre‐processing is done for the input image to eliminate the noise and artefacts present in the image. Then, the pre‐processed image is fed to the feature extraction step where the features are extracted based on convolutional neural network features, and the local pixel pattern‐based texture feature (local PPBTF). Here, the PPBTF is the combination of texture features and pixel pattern‐based features in which the equation of PPBTF is modified based on the local binary pattern. Subsequently, the classification is done based on the extracted features using a deep stacked auto‐encoder, which is trained by the proposed social bat optimisation. The performance of skin cancer detection based on the proposed model is evaluated based on accuracy, sensitivity, and specificity. The proposed model achieves the maximal accuracy of 93.38%, maximal sensitivity of 95%, and the maximal specificity of 96% for K‐fold. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
47. Colon cancer prediction using 2DReCA segmentation and hybrid features on histopathology images.
- Author
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Babu, Tina, Singh, Tripty, and Gupta, Deepa
- Abstract
Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer‐free colon tissue, epithelial cells generally have an elliptical shape; their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi‐level thresholding based on Rényi's two‐dimensional entropy with a cultural algorithm (2DReCA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey‐level co‐occurrence matrix and block‐wise elliptical local binary pattern) are calculated based on pre‐processed grey‐scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
48. Differential Diagnosis of Atypical Hepatocellular Carcinoma in Contrast-Enhanced Ultrasound Using Spatio-Temporal Diagnostic Semantics.
- Author
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Huang, Qinghua, Pan, Fengxin, Li, Wei, Yuan, Feiniu, Hu, Hangtong, Huang, Jinhua, Yu, Jie, and Wang, Wei
- Subjects
CONTRAST-enhanced ultrasound ,HEPATOCELLULAR carcinoma ,DIFFERENTIAL diagnosis ,LIVER cancer ,SUPPORT vector machines - Abstract
Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC. With respect to useful diagnostic semantics, our model automatically calculates three types of semantic feature with equally down-sampled frames based on Contrast-Enhanced Ultrasound (CEUS). Thereafter, a Support Vector Machine (SVM) classifier is trained to make the final diagnosis. Compared with traditional methods for diagnosing HCC, the proposed model has the advantage of less computational complexity and being able to handle the atypical HCC cases. The experimental results show that our method obtained a pretty considerable performance and outperformed two traditional methods. According to the results, the average accuracy reaches 94.40%, recall rate 94.76%, F1-score value 94.62%, specificity 93.62% and sensitivity 94.76%, indicating good merit for automatically diagnosing atypical HCC cases. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
49. Research Data from Bahria University Update Understanding of Breast Cancer (Recognizing Breast Cancer Using Edge-weighted Texture Features of Histopathology Images).
- Subjects
BREAST cancer ,HISTOPATHOLOGY - Abstract
A recent study conducted by researchers at Bahria University in Lahore, Pakistan, has developed a new method for detecting breast cancer using histopathology images. The proposed technique involves converting the images from RGB to YCBCR, extracting texture information using a wavelet transform, and classifying the images with Extreme Gradient Boosting (XGBOOST). The method achieved high accuracy rates on various datasets and suggests that combining wavelet transformation with textural signals can improve breast cancer detection rates and patient outcomes. The research has been peer-reviewed and provides valuable insights into early detection and accurate diagnosis of breast cancer. [Extracted from the article]
- Published
- 2024
50. Development of an intelligent CAD system for mass detection in mammographic images.
- Author
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Andreadis, Theofilos, Emmanouilidis, Christodoulos, Goumas, Stefanos, and Koulouriotis, Dimitrios
- Abstract
Mammography is a very useful tool to diagnose breast cancer in early stages when it is easier to treat. There are two types of evidence that radiologists look for in a mammogram, calcifications and the existence of masses. In this study, an intelligent computer‐aided diagnosis system is proposed for the detection of masses in mammographic images regardless of their nature. The proposed method uses a combination of extended maxima transformations, having different threshold values, in order to find suitable internal and external markers for a marker‐based watershed segmentation. After segmentation, a two‐stage classifier is used to distinguish the masses better from the healthy breast tissue. A feature vector based mainly on contrast and texture features is calculated and two alternative approaches, a Bayesian classifier and a support vector machine (SVM) with Gaussian kernel function, are implemented for further reduction of the false positive areas. The system was evaluated using the data from two online databases. Specifically, 73 mammographic images from the new curated breast imaging subset of digital database for screening mammography (CBIS‐DDSM) database and all the mammographic images that contain masses from the mini‐mammographic image analysis society (MIAS) database were used. The overall sensitivity, in both datasets, was near 80% when the Bayesian classifier was used and above 85% when the SVM was applied. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
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