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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

With the rapid increase in the cases of deadly skin cancer, the classification on different types of skin cancer has been emerging as one of the most significant issues in the field of medical image. Several approaches have been proposed to help in diagnosing the categories of the skin lesions by means of traditional features or leveraging the widely used deep learning models. However, there are lack of the integrated frameworks to combine the hand‐crafted traditional features and the deep Conv‐features. Furthermore, the effective way to extract global and local features is also conducive to distinguish the specific lesions from normal skin. Hence, in this study, the authors present an integrated model to acquire more representative global–local features including the traditional local binary pattern features and deep Conv‐features. In addition, several fusion strategies have conducted on the Global‐DNN and Local‐DNN for better performance. In order to extract more explicit features from the specific lesion areas, a target segmentation method based on visual saliency detection is employed to eliminate the background interference. Experimental results on ISIC‐2017 skin cancer dataset demonstrate that the proposed Global‐DNN and Global‐Local models can obtain more effective feature representation which achieve outperformed results for skin cancer classification. [ABSTRACT FROM AUTHOR]

Chest X-ray (CXR) images are usually used to identify the causes of patients’ symptoms, including the classes of lung or heart disorders. In visualization examination, CXR imaging in anterior–posterior (A–P) views is a preliminary screening method used by clinicians or radiologists to diagnose possible lung abnormalities, such as pneumothorax (Pt), emphysema (E), infiltration (In), lung cancer (M), pneumonia (P), pulmonary fibrosis (F), and pleural effusion (Ef). However, the identification of the causes of multiple abnormalities associated with coexisting conditions presents a challenge. In ruling out a suspected lung disease, the signs and symptoms of physical conditions need to be identified to arrive at a definitive diagnosis. In addition, low contrast CXR images and manual inspection restrict automated screening applications. Hence, this study aims to propose an iterated function system (IFS) and a multilayer fractional-order machine learning classifier to rapidly screen the possible classes of lung diseases within regions of interest on CXR images and to improve screening accuracy. For digital image processes, a two-dimensional (2D) fractional-order convolution is used to enhance symptomatic features. The IFS with nonlinear interpolation functions is then used to reconstruct the 2D feature patterns. These reconstructed patterns are self-affine in the same class and thus help distinguish normal subjects from those with lung diseases. The accuracy rate is thus improved. Pooling is performed to reduce the dimensions of the feature patterns and speed up complex computations. A gray relational analysis-based classifier is used to identify the possible classes of the signs and symptoms of lung diseases. For digital CXR images in A-P view, the proposed multilayer machine learning classifier with k-fold cross-validation presents promising results in screening lung diseases and improving screening accuracy rate relative to traditional methods. The proposed classifier is evaluated in terms of recall (99.6%), precision (87.78%), accuracy (88.88%), and F1 score (0.9334). [ABSTRACT FROM AUTHOR]

Ultrasound (US) imaging is the leading diagnostic method for assessing the early‐stage thyroid nodule. However, the visual evaluation of nodules can be influenced by the subjectivity of radiologists' interpretations. Computer‐aided Diagnostic (CAD) systems can be useful in classifying these nodules according to their benign or malignant nature. The extraction of the characteristics, which relate in the author's case to the US of thyroid nodules, is essential in the differentiation of these nodules. The complex nature of images, however, generates a significant number of features, many of which are either redundant or irrelevant. This study presents a new CAD system that has been developed to categorise thyroid nodules. In this survey, 447 US images of thyroid nodules were retained. These images were used to extract features using statistical features extraction methods. A feature selection method based on the multi objective particle swarm optimisation algorithm was used to choose the most relevant and non‐redundant ones. Then, support vector machine (SVM) and random forests (RFs) were applied to classify these nodules. 10‐fold cross‐validation was used to assess the classification performance metrics. Their proposed CAD has reached a maximum accuracy of 94.28% for SVM; and 96.13% for RF using the contour‐based ROI. [ABSTRACT FROM AUTHOR]

Breast cancer becomes a significant public health problem in the world. During the early detection of breast cancer, it is a very challenging task to classify accurately the benign–malignant patterns in digital mammograms. This study proposes a new fully automated computer‐aided diagnosis (CAD) system for breast cancer diagnosis with high‐accuracy and low‐computational requirements. The expectation–maximisation algorithm is investigated to extract automatically the region of interests (ROIs) within mammograms. The standard shape, statistical, and textural features of ROIs are extracted and combined with multi‐resolution and multi‐orientation features derived from a new feature extraction technique based on wavelet‐based contourlet transform. A hybrid feature selection approach based on combining the support vector machine recursive feature elimination with correlation bias reduction algorithm is proposed. Also, the authors investigate a new similarity‐based learning algorithm, called Q, for benign–malignant classification. The proposed CAD system is applied to real clinical mammograms, and the experimental results demonstrate the superior performance of the proposed CAD system over other existing CAD systems in terms of accuracy 98.16%, sensitivity 98.63%, specificity 97.80%, and computational time 2.2 s. This reveals the effectiveness of the proposed CAD system in improving the accuracy of breast cancer diagnosis in real‐time systems. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to introduce a new species of features to improve the diagnosis efficiency of acute lymphoblastic leukaemia from microscopic images. First, the authors segmented nuclei by the k‐means and watershed algorithms. They extracted three sets of geometrical, statistical, and chaotic features from nuclei images. Six chaotic features were extracted by calculating the fractal dimension from five sub‐images driven from the nuclei images, with their grey levels being modified. The authors classified the images into binary and multiclass types via the support vector machine algorithm. They conducted principal component analysis for dimensional reduction of feature space and then evaluated the proposed algorithm for the overfitting problem. The obtained overall results represent 99% accuracy, 99% specificity, and 97% sensitivity values in the classification of six‐cell groups. The difference between the train and test errors was <3%, which proves that the classification performance had improved by using the multifractal features. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the leading causes of death in the world. Although early detection of lung tumours (nodules) can remarkably diminish the mortal rate, precise detection of them is not always possible by visual inspection of the computerised tomography images. Since nodules with different sizes have non‐uniform shape and brightness, texture attributes and also the gradient of orientation can be good candidate features, which have been used for this purpose. They determined the co‐occurrence matrix of the extended local binary pattern (ELBP) along with weighted orientation difference (WOD) for each sub‐region of the lung area. Local binary pattern is a texture descriptor that can extract the discriminative features efficiently. The proposed ELBP is rotation invariant and suitable to describe non‐uniform patterns. Moreover, WOD as a structural feature uses the magnitude of each edge as the weight of its orientation difference. After constructing the co‐occurrence matrix, discriminative features were extracted from this matrix and fed into a support vector machine in order to classify each sub‐region as a cancerous (nodule) or normal tissue. The proposed method was compared to some of state‐of‐the‐art nodule detection methods and was assessed over several real datasets in terms of specificity, sensitivity and accuracy. [ABSTRACT FROM AUTHOR]

Pectoral muscle segmentation is a very important step in any Computer Aided Detection (CAD) system, for automatic detection of different possible signs of malignancy in the mammographic image. Presence of high‐density glandular tissues overlapping pectoral muscle boundary, presence of pectoral muscle of small size and low density, presence of pectoralis minor etc., poses a great challenge for researchers working on the problem of automatic pectoral muscle boundary detection. Gabor filter has been used by many researchers to detect textural edges. The ability that it can be tuned along a particular direction, makes it a very important image processing tool. In the proposed research work a Gabor filter tuned in the direction of pectoral muscle boundary is applied on the region of interest (ROI) containing pectoral muscle. Finally, the magnitude and phase response of Gabor filtered ROI image along with an edge‐connect and region merge algorithm is used to detect pectoral muscle boundary. The proposed method is validated using 200 mammograms from the miniMIAS database and 200 mammograms from INbreast database. With an accuracy of 97.50% for miniMIAS database and 94.50% for INbreast database, the proposed method outperforms many state‐of‐the‐art methods which show its suitability for CAD Systems. [ABSTRACT FROM AUTHOR]

Classification of benign and malignant pulmonary nodules can provide useful indicators for estimating the risk of lung cancer. In this study, an improved random forest (RF) algorithm is proposed for classification of benign and malignant pulmonary nodules in thoracic computed tomography images. First, an improved random walk algorithm is proposed to automatically segment pulmonary nodules. Then, intensity, geometric and texture features based on the grey‐level co‐occurrence matrix, rotation invariant uniform local binary pattern and Gabor filter methods are combined to generate an effective and discriminative feature vector. Mutual information is employed to reduce the dimensionality. Finally, an improved RF classifier is trained to classify benign and malignant nodules. An appropriate feature subset is selected by the bootstrap method and an effective combination method is introduced to predict a class label. The proposed classification method on the lung images dataset consortium dataset achieves a sensitivity of 0.92 and the area under the receiver‐operating‐characteristic curve of 0.95. An additional evaluation is performed on another dataset coming from General Hospital of Guangzhou Military Command. A mean sensitivity and a mean specificity of the proposed method are 0.85 and 0.82, respectively. Experimental results demonstrate that the proposed method achieves the satisfactory classification performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel automatic computer-aided method to detect polyps for colonoscopy videos. To capture perceptually and semantically meaningful salient polyp regions, we first segment images into multilevel superpixels. Each level corresponds to different sizes of superpixels. Rather than adopting hand-designed features to describe these superpixels in images, we employ sparse autoencoder (SAE) to learn discriminative features in an unsupervised way. Then, a novel unified bottom-up and top-down saliency method is proposed to detect polyps. In the first stage, we propose a weak bottom-up (WBU) saliency map by fusing the contrast-based saliency and object-center-based saliency together. The contrast-based saliency map highlights image parts that show different appearances compared with surrounding areas, whereas the object-center-based saliency map emphasizes the center of the salient object. In the second stage, a strong classifier with multiple kernel boosting is learned to calculate the strong top-down (STD) saliency map based on samples directly from the obtained multilevel WBU saliency maps. We finally integrate these two-stage saliency maps from all levels together to highlight polyps. Experiment results achieve 0.818 recall for saliency calculation, validating the effectiveness of our method. Extensive experiments on public polyp datasets demonstrate that the proposed saliency algorithm performs better compared with state-of-the-art saliency methods to detect polyps. [ABSTRACT FROM AUTHOR]

Automatic identification of abnormal cervical cells, including feature representation, feature combination and classification strategy, is highly demanded in women's annual cervical cancer screenings. However, previous methods only deal with one or two of these three phases, and currently there is few complete framework for this problem. A novel three‐phrase boosting framework is proposed for the detection of abnormal cells from cervical smear images. First, the authors extract 160 dimensional features with respect to each cervical cell from three aspects, including cytology morphology, chromatin pathology and region intensity. In particular, 106 dimensional chromatin pathology features are newly adopted to describe the nucleus textural transformation. Second, an adaptive feature combination method is introduced to select the optimal feature patterns, which can combine all features using a reinforced margin‐based approach with the heuristic knowledge. Finally, a two‐stage classification strategy is presented to reduce erroneous classification abnormal cells using two different classifiers. Experimental results achieve state‐of‐the‐art performance and the proposed framework outperforms the other 16 compared detection methods. [ABSTRACT FROM AUTHOR]

Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality. [ABSTRACT FROM AUTHOR]

Purpose: At present, a one-size-fits-all approach is typically used for cancer therapy in patients. This is mainly because there is no current imaging-based clinical standard for the early assessment and monitoring of cancer treatment response. Here, the authors have developed, for the first time, a complete computer-aided-prognosis (CAP) system based on multiparametric quantitative ultrasound (QUS) spectroscopy methods in association with texture descriptors and advanced machine learning techniques. This system was used to noninvasively categorize and predict cell death levels in fibrosarcoma mouse tumors treated using ultrasound-stimulated microbubbles as novel endothelial-cell radiosensitizers. Methods: Sarcoma xenograft tumor-bearing mice were treated using ultrasound-stimulated microbubbles, alone or in combination with x-ray radiation therapy, as a new antivascular treatment. Therapy effects were assessed at 2-3, 24, and 72 h after treatment using a high-frequency ultrasound. Two-dimensional spectral parametric maps were generated using the power spectra of the raw radiofrequency echo signal. Subsequently, the distances between "pretreatment" and "post-treatment" scans were computed as an indication of treatment efficacy, using a kernel-based metric on textural features extracted from 2D parametric maps. A supervised learning paradigm was used to either categorize cell death levels as low, medium, or high using a classifier, or to "continuously" predict the levels of cell death using a regressor. Results: The developed CAP system performed at a high level for the classification of cell death levels. The area under curve of the receiver operating characteristic was 0.87 for the classification of cell death levels to both low/medium and medium/high levels. Moreover, the prediction of cell death levels using the proposed CAP system achieved a good correlation (r = 0.68, p < 0.001) with histological cell death levels as the ground truth. A statistical test of significance between individual treatment groups with the corresponding control group demonstrated that the predicted levels indicated the same significant changes in cell death as those indicated by the ground-truth levels. Conclusions: The technology developed in this study addresses a gap in the current standard of care by introducing a quality control step that generates potentially actionable metrics needed to enhance treatment decision-making. The study establishes a noninvasive framework for quantifying levels of cancer treatment response developed preclinically in tumors using QUS imaging in conjunction with machine learning techniques. The framework can potentially facilitate the detection of refractory responses in patients to a certain cancer treatment early on in the course of therapy to enable switching to more efficacious treatments. [ABSTRACT FROM AUTHOR]

Purpose: To develop a new algorithm to measure the similarity between the query lung mass and reference lung mass data set for content-based medical image retrieval (CBMIR). Methods: A lung mass data set including 746 mass regions of interest (ROIs) was assembled. Among them, 375 ROIs depicted malignant lesions and 371 depicted benign lesions. Each mass ROI is represented by a vector of 26 texture features. A kernel function was employed to map the original data in input space to a feature space. In this space, a semisupervised distance metric was learned, which used differential scatter discriminant criterion to represent the semantic relevance, and the regularization term to represent the visual similarity. The learned distance metric can measure the similarity of the query mass and reference mass data set. The clustering accuracy is used to configure the parameters. The retrieval accuracy and classification accuracy are used as the performance assessment index. Results: After configuring the parameters, a mean clustering accuracy of 0.87 can be achieved. For retrieval accuracy, our algorithm achieves better performance than other state-of-the-art retrieval algorithms when applying a leave-one-out validation method to the testing data set. For classification accuracy, the area under the ROC curve of our algorithm can be achieved as 0.941±0.006. The running times of 346 query images with the proposed algorithm are 5.399 and 6.0 s, respectively. Conclusions: The study results demonstrated the proposed algorithm outperforms the compared algorithms, when taking the semantic relevant and visual similarity into account in kernel space. The algorithm can be used in a CBMIR system for a query mass to retrieve similarity masses, which can help doctors make better decisions. [ABSTRACT FROM AUTHOR]

Purpose:With raw digital mammograms (DMs), which retain the relationship with x-ray attenuation of the breast tissue, not being routinely available, processed DMs are often the only viable means to acquire imaging measures. The authors investigate differences in quantitative measures of breast density and parenchymal texture, shown to have value in breast cancer risk assessment, between the two DM representations. Methods: The authors report data from 8458 pairs of bilateral raw ("FOR PROCESSING") and processed ("FOR PRESENTATION") DMs acquired from 4278 women undergoing routine screening evaluation, collected with DM units from two different vendors. Breast dense tissue area and percent density (PD), as well as a range of quantitative descriptors of breast parenchymal texture (statistical, co-occurrence, run-length, and structural descriptors), were measured using previously validated, fully automated software. Feature measurements were compared using matched-pairs Wilcoxon signed-ranks test, correlation (r), and linear-mixed-effects (LME) models, where potential interactions with woman- and system-specific factors were also assessed. The authors also compared texture feature correlations with the established risk factors of the Gail lifetime risk score (rG) and breast PD (rPD), and evaluated the within woman intraclass feature correlation (ICC), a measure of bilateral breast-tissue symmetry, in raw versus processed images. Results: All density measures and most of the texture features were strongly (r = 0.6) or moderately (0.4 ≤ r < 0.6) correlated between raw and processed images. However, measurements were significantly different between the two imaging formats (Wilcoxon signed-ranks test, pw < 0.05). The association between measurements varied across features and vendors, and was substantially modified by woman- and system-specific image acquisition factors, such as age, BMI, and mAs/kVp, respectively. The strongest correlation, combined with minimal LME-model interactions, was observed for structural texture features. Overall, texture measures from either image representation were weakly associated with Gail lifetime risk (-0.2 ≤ rG ≤ 0.2), weakly to moderately associated with breast PD (-0.6 ≤ rPD ≤ 0.6), and had overall strong bilateral symmetry (ICC ≥ 0.6). Conclusions: Differences in measures from processed versus raw DM depend highly on the feature, the DM vendor, and image acquisition settings, where structural features appear to be more robust across the different DM settings. The reported findings may serve as a reference in the design of future large-scale studies on mammographic features and breast cancer risk assessment involving multiple DM representations. [ABSTRACT FROM AUTHOR]

Purpose: Statistical modeling of an ultrasound backscattered echo envelope is used for tissue characterization. However, in the presence of complex structures within the analyzed area, estimation of parameters is disturbed and unreliable, e.g., in the case of breast tumor classification. In order to improve the differentiation of breast lesions, the authors proposed a method based on the segmentation of homodyned K distribution parameter maps. Regions within lesions of different scattering properties were extracted and analyzed. In order to improve the classification, the best-performing features were selected from various regions and then combined. Methods: A radio-frequency data set consisting of 103 breast lesions was used in the authors' analysis. Maps of homodyned K distribution parameters were created using an algorithm based on signal-to-noise ratio, kurtosis, and skewness of fractional-order envelope moments. A Markov random field model was used to segment parametric maps. Features of different segments were extracted and evaluated based on bootstrapping and the receiver operating characteristic curve. To determine the best-performing feature subset, the authors applied the joint mutual information criterion. Results: It was found that there were individual features which performed better than the ones commonly used for lesion characterization, like the parameter obtained through averaging of values over the whole lesion. The authors selected and discussed the best-performing features. Properties of different extracted regions were important and improved the distinction between benign and malignant tumors. The best performance was obtained by combining four features with the area under the receiver operating curve of 0.84. Conclusions: The study showed that the analysis of internal changes in lesion parametric maps leads to a better classification of breast tumors. The authors recommend combining multiple features for characterization, instead of using only one parameter, especially in the case of heterogeneous lesions. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. Methods: This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. Results: The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q = 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. Conclusions: As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself. [ABSTRACT FROM AUTHOR]

Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations. [ABSTRACT FROM AUTHOR]

Purpose: Precisely and efficiently locating a tumor with less manual intervention in ultrasound-guided high-intensity focused ultrasound (HIFU) therapy is one of the keys to guaranteeing the therapeutic result and improving the efficiency of the treatment. The segmentation of ultrasound images has always been difficult due to the influences of speckle, acoustic shadows, and signal attenuation as well as the variety of tumor appearance. The quality of HIFU guidance images is even poorer than that of conventional diagnostic ultrasound images because the ultrasonic probe used for HIFU guidance usually obtains images without making contact with the patient’s body. Therefore, the segmentation becomes more difficult. To solve the segmentation problem of ultrasound guidance image in the treatment planning procedure for HIFU therapy, a novel region-based segmentation method for uterine fibroids in HIFU guidance images is proposed. Methods: Tumor partitioning in HIFU guidance image without manual intervention is achieved by a region-based split-and-merge framework. A new iterative multiple region growing algorithm is proposed to first split the image into homogenous regions (superpixels). The features extracted within these homogenous regions will be more stable than those extracted within the conventional neighborhood of a pixel. The split regions are then merged by a superpixel-based adaptive spectral clustering algorithm. To ensure the superpixels that belong to the same tumor can be clustered together in the merging process, a particular construction strategy for the similarity matrix is adopted for the spectral clustering, and the similarity matrix is constructed by taking advantage of a combination of specifically selected first-order and second-order texture features computed from the gray levels and the gray level co-occurrence matrixes, respectively. The tumor region is picked out automatically from the background regions by an algorithm according to a priori information about the tumor position, shape, and size. Additionally, an appropriate cluster number for spectral clustering can be determined by the same algorithm, thus the automatic segmentation of the tumor region is achieved. Results: To evaluate the performance of the proposed method, 50 uterine fibroid ultrasound images from different patients receiving HIFU therapy were segmented, and the obtained tumor contours were compared with those delineated by an experienced radiologist. For area-based evaluation results, the mean values of the true positive ratio, the false positive ratio, and the similarity were 94.42%, 4.71%, and 90.21%, respectively, and the corresponding standard deviations were 2.54%, 3.12%, and 3.50%, respectively. For distance-based evaluation results, the mean values of the normalized Hausdorff distance and the normalized mean absolute distance were 4.93% and 0.90%, respectively, and the corresponding standard deviations were 2.22% and 0.34%, respectively. The running time of the segmentation process was 12.9 s for a 318×333 (pixels) image. Conclusions: Experiments show that the proposed method can segment the tumor region accurately and efficiently with less manual intervention, which provides for the possibility of automatic segmentation and real-time guidance in HIFU therapy. [ABSTRACT FROM AUTHOR]

Image textures in computed tomography colonography (CTC) have great potential for differentiating non-neoplastic from neoplastic polyps and thus can advance the current CTC detection-only paradigm to a new level with diagnostic capability. However, image textures are frequently compromised, particularly in low-dose CT imaging. Furthermore, texture feature extraction may vary, depending on the polyp spatial orientation variation, resulting in variable results. To address these issues, this study proposes an adaptive approach to extract and analyze the texture features for polyp differentiation. Firstly, derivative (e.g. gradient and curvature) operations are performed on the CT intensity image to amplify the textures with adequate noise control. Then Haralick co-occurrence matrix (CM) is used to calculate texture measures along each of the 13 directions (defined by the first and second order image voxel neighbors) through the polyp volume in the intensity, gradient and curvature images. Instead of taking the mean and range of each CM measure over the 13 directions as the so-called Haralick texture features, Karhunen-Loeve transform is performed to map the 13 directions into an orthogonal coordinate system so that the resulted texture features are less dependent on the polyp orientation variation. These simple ideas for amplifying textures and stabilizing spatial variation demonstrated a significant impact for the differentiating task by experiments using 384 polyp datasets, of which 52 are non-neoplastic polyps and the rest are neoplastic polyps. By the merit of area under the curve of receiver operating characteristic, the innovative ideas achieved differentiation capability of 0.8016, indicating the CTC diagnostic feasibility. [ABSTRACT FROM AUTHOR]

Purpose: Increasing evidence suggests radiomics features extracted from computed tomography (CT) images may be useful in prognostic models for patients with nonsmall cell lung cancer (NSCLC). This study was designed to determine whether such features can be reproducibly obtained from cone-beam CT (CBCT) images taken using medical Linac onboard-imaging systems in order to track them through treatment. Methods: Test-retest CBCT images of ten patients previously enrolled in a clinical trial were retrospectively obtained and used to determine the concordance correlation coefficient (CCC) for 68 different texture features. The volume dependence of each feature was also measured using the Spearman rank correlation coefficient. Features with a high reproducibility (CCC > 0.9) that were not due to volume dependence in the patient test-retest set were further examined for their sensitivity to differences in imaging protocol, level of scatter, and amount of motion by using two phantoms. The first phantom was a texture phantom composed of rectangular cartridges to represent different textures. Features were measured from two cartridges, shredded rubber and dense cork, in this study. The texture phantom was scanned with 19 different CBCT imagers to establish the features' interscanner variability. The effect of scatter on these features was studied by surrounding the same texture phantom with scattering material (rice and solid water). The effect of respiratory motion on these features was studied using a dynamic-motion thoracic phantom and a specially designed tumor texture insert of the shredded rubber material. The differences between scans acquired with different Linacs and protocols, varying amounts of scatter, and with different levels of motion were compared to the mean intrapatient difference from the test-retest image set. Results: Of the original 68 features, 37 had a CCC >0.9 that was not due to volume dependence. When the Linac manufacturer and imaging protocol were kept consistent, 4-13 of these 37 features passed our criteria for reproducibility more than 50% of the time, depending on the manufacturerprotocol combination. Almost all of the features changed substantially when scatter material was added around the phantom. For the dense cork, 23 features passed in the thoracic scans and 11 features passed in the head scans when the differences between one and two layers of scatter were compared. Using the same test for the shredded rubber, five features passed the thoracic scans and eight features passed the head scans. Motion substantially impacted the reproducibility of the features. With 4 mm of motion, 12 features from the entire volume and 14 features from the center slice measurements were reproducible. With 6-8 mm of motion, three features (Laplacian of Gaussian filtered kurtosis, gray-level nonuniformity, and entropy), from the entire volume and seven features (coarseness, high gray-level run emphasis, gray-level nonuniformity, sum-average, information measure correlation, scaled mean, and entropy) from the center-slice measurements were considered reproducible. Conclusions: Some radiomics features are robust to the noise and poor image quality of CBCT images when the imaging protocol is consistent, relative changes in the features are used, and patients are limited to those with less than 1 cm of motion. [ABSTRACT FROM AUTHOR]

Purpose: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain cancer. Four molecular subtypes of GBM have been described but can only be determined by an invasive brain biopsy. The goal of this study is to evaluate the utility of texture features extracted from magnetic resonance imaging (MRI) scans as a potential noninvasive method to characterize molecular subtypes of GBM and to predict 12-month overall survival status for GBM patients. Methods: The authors manually segmented the tumor regions from postcontrast T1 weighted and T2 fluid-attenuated inversion recovery (FLAIR) MRI scans of 82 patients with de novo GBM. For each patient, the authors extracted five sets of computer-extracted texture features, namely, 48 segmentation-based fractal texture analysis (SFTA) features, 576 histogram of oriented gradients (HOGs) features, 44 run-length matrix (RLM) features, 256 local binary patterns features, and 52 Haralick features, from the tumor slice corresponding to the maximum tumor area in axial, sagittal, and coronal planes, respectively. The authors used an ensemble classifier called random forest on each feature family to predict GBM molecular subtypes and 12-month survival status (a dichotomized version of overall survival at the 12-month time point indicating if the patient was alive or not at 12 months). The performance of the prediction was quantified and compared using receiver operating characteristic (ROC) curves. Results: With the appropriate combination of texture feature set, image plane (axial, coronal, or sagittal), and MRI sequence, the area under ROC curve values for predicting different molecular subtypes and 12-month survival status are 0.72 for classical (with Haralick features on T1 postcontrast axial scan), 0.70 for mesenchymal (with HOG features on T2 FLAIR axial scan), 0.75 for neural (with RLM features on T2 FLAIR axial scan), 0.82 for proneural (with SFTA features on T1 postcontrast coronal scan), and 0.69 for 12-month survival status (with SFTA features on T1 postcontrast coronal scan). Conclusions: The authors evaluated the performance of five types of texture features in predicting GBM molecular subtypes and 12-month survival status. The authors' results show that texture features are predictive of molecular subtypes and survival status in GBM. These results indicate the feasibility of using tumor-derived imaging features to guide genomically informed interventions without the need for invasive biopsies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: Triple-negative breast cancer (TNBC), an aggressive subtype, is frequently misclassified as fibroadenoma due to benign morphologic features on breast ultrasound (US). This study aims to develop a computer-aided diagnosis (CAD) system based on texture features for distinguishing between TNBC and benign fibroadenomas in US images. Methods: US images of 169 pathology-proven tumors (mean size, 1.65 cm; range, 0.7-3.0 cm) composed of 84 benign fibroadenomas and 85 TNBC tumors are used in this study. After a tumor is segmented out using the level-set method, morphological, conventional texture, and multiresolution gray-scale invariant texture feature sets are computed using a best-fitting ellipse, gray-level co-occurrence matrices, and the ranklet transform, respectively. The linear support vector machine with leave-one-out cross-validation schema is used as a classifier, and the diagnostic performance is assessed with receiver operating characteristic curve analysis. Results: The Az values of the morphology, conventional texture, and multiresolution gray-scale invariant texture feature sets are 0.8470 [95% confidence intervals (CIs), 0.7826-0.8973], 0.8542 (95% CI, 0.7911-0.9030), and 0.9695 (95% CI, 0.9376-0.9865), respectively. The Az of the CAD system based on the combined feature sets is 0.9702 (95% CI, 0.9334-0.9882). Conclusions: The CAD system based on texture features extracted via the ranklet transform may be useful for improving the ability to discriminate between TNBC and benign fibroadenomas. [ABSTRACT FROM AUTHOR]

Purpose: Lung dynamic MRI (dMRI) has emerged to be an appealing tool to quantify lung motion for both planning and treatment guidance purposes. However, this modality can result in blurry images due to intrinsically low signal-to-noise ratio in the lung and spatial/temporal interpolation. The image blurring could adversely affect the image processing that depends on the availability of fine landmarks. The purpose of this study is to reduce dMRI blurring using image postprocessing. Methods: To enhance the image quality and exploit the spatiotemporal continuity of dMRI sequences, a low-rank decomposition and dictionary learning (LDDL) method was employed to deblur lung dMRI and enhance the conspicuity of lung blood vessels. Fifty frames of continuous 2D coronal dMRI frames using a steady state free precession sequence were obtained from five subjects including two healthy volunteer and three lung cancer patients. In LDDL, the lung dMRI was decomposed into sparse and low-rank components. Dictionary learning was employed to estimate the blurring kernel based on the whole image, low-rank or sparse component of the first image in the lung MRI sequence. Deblurring was performed on the whole image sequences using deconvolution based on the estimated blur kernel. The deblurring results were quantified using an automated blood vessel extraction method based on the classification of Hessian matrix filtered images. Accuracy of automated extraction was calculated using manual segmentation of the blood vessels as the ground truth. Results: In the pilot study, LDDL based on the blurring kernel estimated from the sparse component led to performance superior to the other ways of kernel estimation. LDDL consistently improved image contrast and fine feature conspicuity of the original MRI without introducing artifacts. The accuracy of automated blood vessel extraction was on average increased by 16% using manual segmentation as the ground truth. Conclusions: Image blurring in dMRI images can be effectively reduced using a low-rank decomposition and dictionary learning method using kernels estimated by the sparse component. [ABSTRACT FROM AUTHOR]

Purpose: Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (IBEX), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions. Methods: The IBEX software package was developed using the MATLAB and c / c + + programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand, IBEX is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand, IBEX provides an integrated development environment on top of MATLAB and c / c + +, so users are not limited to its built-in functions. In the IBEX developer studio, users can plug in, debug, and test new algorithms, extending IBEX'S functionality. IBEX also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the IBEX workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions. Results: Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the IBEX software to be intuitive, powerful, and easy to use. IBEX can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone IBEX and IBEX'S source code can be downloaded. Conclusions: The authors successfully implemented IBEX, an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation. © 2015 American Association of Physicists in Medicine. [ABSTRACT FROM AUTHOR]

Purpose: Ultrasound-based solutions for diagnosis and prognosis of prostate cancer are highly desirable. The authors have devised a method for detecting prostate cancer using a vibroelastography (VE) system developed in our group and a tissue classification approach based on texture analysis of VE images. Methods: The VE method applies wide-band mechanical vibrations to the tissue. Here, the authors report on the use of this system for cancer detection and show that the texture of VE images characterized by the first and the second order statistics of the pixel intensities form a promising set of features for tissue typing to detect prostate cancer. The system was used to image patients prior to radical surgery. The removed specimens were sectioned and studied by an experienced histopathologist. The authors registered the whole-mount histology sections to the ultrasound images using an automatic registration algorithm. This enabled the quantitative evaluation of the performance of the authors' imaging method in cancer detection in an unbiased manner. The authors used support vector machine (SVM) classification to measure the cancer detection performance of the VE method. Regions of tissue of size 5 × 5 mm, labeled as cancer and noncancer based on automatic registration to histology slides, were classified using SVM. Results: The authors report an area under ROC of 0.81 ± 0.10 in cancer detection on 1066 tissue regions from 203 images. All cancer tumors in all zones were included in this analysis and were classified versus the noncancer tissue in the peripheral zone. This outcome was obtained in leaveone- patient-out validation. Conclusions: The developed 3D prostate vibroelastography system and the proposed multiparametric approach based on statistical texture parameters from the VE images result in a promising cancer detection method. [ABSTRACT FROM AUTHOR]

Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers: k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were, respectively, 2.48 ± 2.19 and 8.32 ± 5.66 mm, whereas these distances were 1.66 ± 1.29 and 5.75 ± 4.88 mm between the segmentations by the reference reader and the independent observer, respectively. The automatic segmentations were also visually assessed by two trained CXR readers as "excellent," "adequate," or "insufficient." The readers had good agreement in assessing the cavity outlines and 84% of the segmentations were rated as "excellent" or "adequate" by both readers. Conclusions: The proposed cavity segmentation technique produced results with a good degree of overlap with manual expert segmentations. The evaluation measures demonstrated that the results approached the results of the experienced chest radiologists, in terms of overlap measure and contour distance measures. Automatic cavity segmentation can be employed in TB clinics for treatment monitoring, especially in resource limited settings where radiologists are not available. [ABSTRACT FROM AUTHOR]

Purpose: Tumor grading is an important part of breast cancer diagnosis and currently requires biopsy as its standard. Here, the authors investigate quantitative ultrasound parameters in locally advanced breast cancers that can potentially separate tumors from normal breast tissue and differentiate tumor grades. Methods: Ultrasound images and radiofrequency data from 42 locally advanced breast cancer patients were acquired and analyzed. Parameters related to the linear regression of the power spectrum-midband fit, slope, and 0-MHz-intercept-were determined from breast tumors and normal breast tissues. Mean scatterer spacing was estimated from the spectral autocorrelation, and the effective scatterer diameter and effective acoustic concentration were estimated from the Gaussian form factor. Parametric maps of each quantitative ultrasound parameter were constructed from the gated radiofrequency segments in tumor and normal tissue regions of interest. In addition to the mean values of the parametric maps, higher order statistical features, computed from gray-level co-occurrence matrices were also determined and used for characterization. Finally, linear and quadratic discriminant analyses were performed using combinations of quantitative ultrasound parameters to classify breast tissues. Results: Quantitative ultrasound parameters were found to be statistically different between tumor and normal tissue (p < 0.05). The combination of effective acoustic concentration and mean scatterer spacing could separate tumor from normal tissue with 82% accuracy, while the addition of effective scatterer diameter to the combination did not provide significant improvement (83% accuracy). Furthermore, the two advanced parameters, including effective scatterer diameter and mean scatterer spacing, were found to be statistically differentiating among grade I, II, and III tumors (p = 0.014 for scatterer spacing, p = 0.035 for effective scatterer diameter). The separation of the tumor grades further improved when the textural features of the effective scatterer diameter parametric map were combined with the mean value of the map (p = 0.004). Conclusions: Overall, the binary classification results (tumor versus normal tissue) were more promising than tumor grade assessment. Combinations of advanced parameters can further improve the separation of tumors from normal tissue compared to the use of linear regression parameters. While the linear regression parameters were sufficient for characterizing breast tumors and normal breast tissues, advanced parameters and their textural features were required to better characterize tumor subtypes. [ABSTRACT FROM AUTHOR]

Modified Bloom–Richardson (mBR) grading is known to have prognostic value in breast cancer (BCa), yet its use in clinical practice has been limited by intra- and interobserver variability. The development of a computerized system to distinguish mBR grade from entire estrogen receptor-positive (ER+) BCa histopathology slides will help clinicians identify grading discrepancies and improve overall confidence in the diagnostic result. In this paper, we isolate salient image features characterizing tumor morphology and texture to differentiate entire hematoxylin and eosin (H and E) stained histopathology slides based on mBR grade. The features are used in conjunction with a novel multi-field-of-view (multi-FOV) classifier—a whole-slide classifier that extracts features from a multitude of FOVs of varying sizes—to identify important image features at different FOV sizes. Image features utilized include those related to the spatial arrangement of cancer nuclei (i.e., nuclear architecture) and the textural patterns within nuclei (i.e., nuclear texture). Using slides from 126 ER+ patients (46 low, 60 intermediate, and 20 high mBR grade), our grading system was able to distinguish low versus high, low versus intermediate, and intermediate versus high grade patients with area under curve values of 0.93, 0.72, and 0.74, respectively. Our results suggest that the multi-FOV classifier is able to 1) successfully discriminate low, medium, and high mBR grade and 2) identify specific image features at different FOV sizes that are important for distinguishing mBR grade in H and E stained ER+ BCa histology slides. [ABSTRACT FROM PUBLISHER]

In this paper, we investigated the behavior of 22 co-occurrence statistics combined to six gray-scale quantization levels to classify breast lesions on ultrasound (BUS) images. The database of 436 BUS images used in this investigation was formed by 217 carcinoma and 219 benign lesions images. The region delimited by a minimum bounding rectangle around the lesion was employed to calculate the gray-level co-occurrence matrix (GLCM). Next, 22 co-occurrence statistics were computed regarding six quantization levels (8, 16, 32, 64, 128, and 256), four orientations (0° , 45° , 90° , and 135° ), and ten distances (1, 2,...,10 pixels). Also, to reduce feature space dimensionality, texture descriptors of the same distance were averaged over all orientations, which is a common practice in the literature. Thereafter, the feature space was ranked using mutual information technique with minimal-redundancy-maximal-relevance (mRMR) criterion. Fisher linear discriminant analysis (FLDA) was applied to assess the discrimination power of texture features, by adding the first m-ranked features to the classification procedure iteratively until all of them were considered. The area under ROC curve (AUC) was used as figure of merit to measure the performance of the classifier. It was observed that averaging texture descriptors of a same distance impacts negatively the classification performance, since the best AUC of 0.81 was achieved with 32 gray levels and 109 features. On the other hand, regarding the single texture features (i.e., without averaging procedure), the quantization level does not impact the discrimination power, since AUC=0.87 was obtained for the six quantization levels. Moreover, the number of features was reduced (between 17 and 24 features). The texture descriptors that contributed notably to distinguish breast lesions were contrast and correlation computed from GLCMs with orientation of 90° and distance more than five pixels. [ABSTRACT FROM AUTHOR]

Automatic classification of lesions for gastroenterology imaging scenarios poses novel challenges to computer-assisted decision systems, which are mostly attributed to the dynamics of the image acquisition conditions. Such challenges demand that automatic systems are able to give robust characterizations of tissues irrespective of camera rotation, zoom, and illumination gradients when viewing the inner surface of the gastrointestinal tract. In this paper, we study the invariance properties of Gabor filters and propose a novel descriptor, the autocorrelation Gabor features (AGF). We show that our proposed AGF is invariant to scale, rotation, and illumination changes in the images. We integrate these new features in a texton framework (Texton-AGF) to classify images from two complementary gastroenterology imaging scenarios (chromoendoscopy and narrow-band imaging) broadly into three different groups: normal, precancerous, and cancerous. Results show that they compare favorably to using state-of-the-art texture descriptors for both imaging modalities. [ABSTRACT FROM PUBLISHER]

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists' gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists' gaze behavior and image content. [ABSTRACT FROM AUTHOR]