Search

Segmentation of the pectoral muscle is one of the most fundamental steps in developing a Computer-Aided Diagnosis (CAD) System. Considering the presence of various artifacts and homogeneity among the breast region, the pectoral muscle segmentation is a relatively tricky task. This study proposes a modified U-Net structure for automatic pectoral muscle segmentation from mammogram images. The modified U-Net architecture is different from the traditional U-Net architecture in having densely connected convolution layers. The proposed architecture was trained, validated, and tested by utilizing 322 and 200 mammogram images from two publicly available datasets: Mammographic Image Analysis Society (mini-MIAS) and the INBreast. Mini-MIAS contains scanned filmed mammograms, whereas INBreast contains full-field digital (FFDM) mammograms. Unlike other segmentation techniques, the modified U-Net architecture proposed does not require any pre-processing method and performs equally well on both scanned filmed and FFDM mammograms. We tested the system by fusing both the above datasets to verify our claim. Our proposed architecture has obtained satisfactory results on the mixed dataset. The experimental results showed that the pectoral muscle segmentation approach received a dice coefficient of 96.38 ± 1.52 and 96.82 ± 2.23 on mini-MIAS and the INBreast databases, respectively. Moreover on mixing both the datasets and randomly testing 20% of the samples, the algorithm achieved a Dice coefficient of 95.82 ± 1.23. [ABSTRACT FROM AUTHOR]

Abstract Background The indulgence of artificial intelligence (AI) has been considered recently in the work up for the detection and diagnosis of breast cancer through algorithms that could supply diagnosis as the radiologist do. The algorithm learns from a supervised and continuous input of large and new data sets unlike the standard programming, which requires clear step-by-step instructions. The aim of this study is to assess the ability of AI scanned mammograms to aid the ultrasound in the discrimination between phyllodes tumors and fibro-adenomas. Results This is a retrospective analysis included 374 proven phyllodes tumors (PT) and fibro-adenomas (FA). Digital mammogram and breast ultrasound was performed for all the cases and each breast was given a “Breast Imaging Reporting and Data System” (BI-RADS) score. Included mammograms were scanned by AI with resultant a qualitative heatmap and a quantitative abnormality scoring of suspicion percentage. The study included 164 PT (43.9%) and 210 FA (56.1%). BI-RADS category 2 was assigned in 40.1%, category 3 in 38.2%, category 4 in 18.5% and category 5 in 3.2% with median value of the AI abnormality scoring of 23%, 44%, 65% and 90% respectively. Sensitivity and specificity of the conventional imaging were 59.2% and 75.8% respectively. The AI abnormality scoring of 49.5% upgraded the sensitivity to 89.6% and specificity to 94.8% in the ability to discriminate PT from FA masses. Conclusion Artificial intelligence-aided mammograms could be used as method of distinction between PT from FA detected on sono-mammogram. The color hue and the quantification of the abnormality scoring percentage could be used as a one setting method for specification and so guide clinicians in their decision of conservative management or the choice of the surgical procedure.

Breast cancer is the most common cancer with highest mortality risk among the female worldwide and breast mass is the most effective sign for cancer identification. Thus, accurate segmentation of breast mass is regarded as a key step to reduce the death rate. Traditional segmentation methods require prior knowledge and manually set parameters, while recent studies prefer to construct neural networks based on feature reuse. However, breast mass can display in different orientations and the spatial context is complex, which makes the segmentation remain a challenging task. For these concerns, we propose a Spatial Enhanced Rotation Aware Network (SERAN) for automatic breast mass segmentation. SERAN consists of two critical components: 1) a residual attention encoder with spatial enhancement mechanism for effective feature extraction, and 2) a decoder constructed by multi-stream rotation aware blocks for feature fusion and prediction refinement. To optimize SERAN better and avoid misclassification in background area, a regulation item named Inside-outside Loss (IOL) is used in training procedure. The experimental results tested on a representative subset of Digital Database for Screening Mammography (DDSM) dataset show that SERAN outperforms state-of-the-art methods among most adopted evaluation metrics.

Background: The indulgence of artificial intelligence (AI) has been considered recently in the work up for the detection and diagnosis of breast cancer through algorithms that could supply diagnosis as the radiologist do. The algorithm learns from a supervised and continuous input of large and new data sets unlike the standard programming, which requires clear step-by-step instructions. The aim of this study is to assess the ability of AI scanned mammograms to aid the ultrasound in the discrimination between phyllodes tumors and fibro-adenomas. Results: This is a retrospective analysis included 374 proven phyllodes tumors (PT) and fibro-adenomas (FA). Digital mammogram and breast ultrasound was performed for all the cases and each breast was given a "Breast Imaging Reporting and Data System" (BI-RADS) score. Included mammograms were scanned by AI with resultant a qualitative heatmap and a quantitative abnormality scoring of suspicion percentage. The study included 164 PT (43.9%) and 210 FA (56.1%). BI-RADS category 2 was assigned in 40.1%, category 3 in 38.2%, category 4 in 18.5% and category 5 in 3.2% with median value of the AI abnormality scoring of 23%, 44%, 65% and 90% respectively. Sensitivity and specificity of the conventional imaging were 59.2% and 75.8% respectively. The AI abnormality scoring of 49.5% upgraded the sensitivity to 89.6% and specificity to 94.8% in the ability to discriminate PT from FA masses. Conclusion: Artificial intelligence-aided mammograms could be used as method of distinction between PT from FA detected on sono-mammogram. The color hue and the quantification of the abnormality scoring percentage could be used as a one setting method for specification and so guide clinicians in their decision of conservative management or the choice of the surgical procedure. [ABSTRACT FROM AUTHOR]

Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among the region of the breast and pectoral muscle, and low contrast along the region of breast boundary, the similarity between the texture of the Region of Interest (ROI), and the unwanted region and irregular ROI. This study aims to propose an improved threshold-based and trainable segmentation model to derive ROI. A hybrid segmentation approach for the boundary of the breast region and pectoral muscle in mammogram images was established based on thresholding and Machine Learning (ML) techniques. For breast boundary estimation, the region of the breast was highlighted by eliminating bands of the wavelet transform. The initial breast boundary was determined through a new thresholding technique. Morphological operations and masking were employed to correct the overestimated boundary by deleting small objects. In the medical imaging field, significant progress to develop effective and accurate ML methods for the segmentation process. In the literature, the imperative role of ML methods in enabling effective and more accurate segmentation method has been highlighted. In this study, an ML technique was built based on the Histogram of Oriented Gradient (HOG) feature with neural network classifiers to determine the region of pectoral muscle and ROI. The proposed segmentation approach was tested by utilizing 322, 200, 100 mammogram images from mammographic image analysis society (mini-MIAS), INbreast, Breast Cancer Digital Repository (BCDR) databases, respectively. The experimental results were compared with manual segmentation based on different texture features. Moreover, evaluation and comparison for the boundary of the breast region and pectoral muscle segmentation have been done separately. The experimental results showed that the boundary of the breast region and the pectoral muscle segmentation approach obtained an accuracy of 98.13% and 98.41% (mini-MIAS), 100%, and 98.01% (INbreast), and 99.8% and 99.5% (BCDR), respectively. On average, the proposed study achieved 99.31% accuracy for the boundary of breast region segmentation and 98.64% accuracy for pectoral muscle segmentation. The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation. More so, this article also included the ground-truth as an evaluation of comprehensive similarity. In the clinic, this analysis may be provided as a valuable support for breast cancer identification.

Abstract Background The percentage of mammographic dense tissue (PD) defined by pixel value threshold is a well-established risk factor for breast cancer. Recently there has been some evidence to suggest that an increased threshold based on visual assessment could improve risk prediction. It is unknown, however, whether this also applies to volumetric density using digital raw mammograms. Method Two case-control studies nested within a screening cohort (ages of participants 46–73 years) from Manchester UK were used. In the first study (317 cases and 947 controls) cases were detected at the first screen; whereas in the second study (318 cases and 935 controls), cases were diagnosed after the initial mammogram. Volpara software was used to estimate dense tissue height at each pixel point, and from these, volumetric and area-based PD were computed at a range of thresholds. Volumetric and area-based PDs were evaluated using conditional logistic regression, and their predictive ability was assessed using the Akaike information criterion (AIC) and matched concordance index (mC). Results The best performing volumetric PD was based on a threshold of 5 mm of dense tissue height (which we refer to as VPD5), and the best areal PD was at a threshold level of 6 mm (which we refer to as APD6), using pooled data and in both studies separately. VPD5 showed a modest improvement in prediction performance compared to the original volumetric PD by Volpara with ΔAIC = 5.90 for the pooled data. APD6, on the other hand, shows much stronger evidence for better prediction performance, with ΔAIC = 14.52 for the pooled data, and mC increased slightly from 0.567 to 0.577. Conclusion These results suggest that imposing a 5 mm threshold on dense tissue height for volumetric PD could result in better prediction of cancer risk. There is stronger evidence that area-based density with a 6 mm threshold gives better prediction than the original volumetric density metric.

Abstract Background The percentage of mammographic dense tissue (PD) is an important risk factor for breast cancer, and there is some evidence that texture features may further improve predictive ability. However, relatively little work has assessed or validated textural feature algorithms using raw full field digital mammograms (FFDM). Method A case-control study nested within a screening cohort (age 46–73 years) from Manchester UK was used to develop a texture feature risk score (264 cases diagnosed at the same time as mammogram of the contralateral breast, 787 controls) using the least absolute shrinkage and selection operator (LASSO) method for 112 features, and validated in a second case-control study from the same cohort but with cases diagnosed after the index mammogram (317 cases, 931 controls). Predictive ability was assessed using deviance and matched concordance index (mC). The ability to improve risk estimation beyond percent volumetric density (Volpara) was evaluated using conditional logistic regression. Results The strongest features identified in the training set were “sum average” based on the grey-level co-occurrence matrix at low image resolutions (original resolution 10.628 pixels per mm; downsized by factors of 16, 32 and 64), which had a better deviance and mC than volumetric PD. In the validation study, the risk score combining the three sum average features achieved a better deviance than volumetric PD (Δχ2 = 10.55 or 6.95 if logarithm PD) and a similar mC to volumetric PD (0.58 and 0.57, respectively). The risk score added independent information to volumetric PD (Δχ2 = 14.38, p = 0.0008). Conclusion Textural features based on digital mammograms improve risk assessment beyond volumetric percentage density. The features and risk score developed need further investigation in other settings.

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (95.00 = 0.57%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an Az value equal to 0.97 = 0.01. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method can be used for detecting suspected incipient breast lesions that may cause breast cancer. The image processing of mammograms is very important for the early detection of breast pathologies, including cancer. This paper proposes a new technique based on directional fractal filtering for detecting microcalcification clusters or irregularly shaped microcalcifications. The proposed algorithm has two parts: a preprocessing step for detecting and locating microcalcification; and a second zooming, enhancement, and segmentation step. Detection is performed by image convolution using a set of masks with interesting fractal properties. Combined with other simple mathematical operations, remarkable contrast enhancement and segmentation are produced. The final result permits the clear delineation of the shape of individual microcalcifications. A comparison is made with other microcalcification enhancement techniques described in the literature. [ABSTRACT FROM AUTHOR]

Early detection is a key step for effective treatment of breast cancer and computer-aided diagnosis (CAD) is the most common tool used by the medical research community to detect early breast cancer development. Automated and accurate classification of mammogram images is an important criterion for the analysis and interpretation of these images and many methods have been proposed in this direction. In this paper, an improved CAD model is developed to classify the digital mammograms into normal and abnormal, and further, benign and malignant. The proposed model constitutes four different phases, namely, region of interest (ROI) generation, feature extraction, feature reduction, and classification. The proposed model first employs discrete Tchebichef transform (DTT) to extract the features from the ROIs. Subsequently, a technique based on a combination of principal component analysis (PCA) and linear discriminant analysis (LDA) is employed to reduce the dimensions of the feature vector. Next, the reduced features are sent to an extreme learning machine (ELM) for the classification. Here, to obtain a better generalization performance, the hidden node parameters of ELM are optimized through an improved grey wolf optimization-based ELM (IGWO-ELM). To validate the proposed CAD system, different performance metrics such as accuracy, sensitivity, specificity, and area under curve (AUC) are measured using k-fold stratified cross-validation (SCV). Moreover, to eliminate the issue of randomness, 10 independent runs are carried out on SCV. From a detailed analysis of the results, it is observed that the proposed model yields an average accuracy of 100% for MIAS dataset in both normal vs. abnormal, and benign vs. malignant cases. Further, the accuracy achieved for DDSM dataset is 99.50%, and 98.50% for normal vs. abnormal, and benign vs. malignant cases, respectively. The computation time taken by the proposed CAD model for MIAS and DDSM are 1.131 secs and 3.063 secs, respectively. The experimental analysis justifies the effectiveness of the proposed CAD model and as a result, this model can be considered as an effective tool to help the radiologists for better diagnosis. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common malignant diseases among women. Mammography is at present one of the available methods for early detection of abnormalities, which is related to breast cancer different lesions to show characteristics such as masses, which can be detected through this technique. The images are divided according to the Mini-MIAS database. In order to classify the cluster as malignant or benign, the 2nd order co-occurrence statistical properties of the image such as contrast, correlation, homogeneity, and energy were adopted. The gray-scale convergent matrices (GLCM) are used with a suggested feature of gray level density matrices (GLDM) to identify abnormal tissues (malignant) and natural tissue (benign). The proposed method of analysis is tested on several images (taken from a UK organization interested in understanding breast X-ray images) from the database of digital mammograms and outstanding results were obtained for breast cancer detection. The proposed multilayered design performance significantly improved the diagnosis of breast cancer by more than 95% and 95.8% sensitivity, 95.5% specificity for all datasets, 91.1% with 100% sensitivity and 84% specificity for 70% training data and 30% testing data. [ABSTRACT FROM AUTHOR]

Breast cancer is the most life-threatening disease among women. The best way to decrease the mortality is early detection of cancer from digital mammogram. The diagnosis can be successful if the pre-processing and segmentation of the digital mammograms identifies the suspicious area correctly. In this paper, the Butterworth bandpass filter along with fusion of FCM and K-means clustering followed by morphological operations is used for the segmentation of calcification areas from the mammogram images. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the life-threatening cancers occurring in women. In recent years, from the surveys provided by various medical organizations, it has become clear that the mortality rate of females is increasing owing to the late detection of breast cancer. Therefore, an automated algorithm is needed to identify the early occurrence of microcalcification, which would assist radiologists and physicians in reducing the false predictions via image processing techniques. In this work, we propose a new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics. The considered physical characteristics are the reflection coefficient and mass density of the binned digital mammogram image. The calculation of physical characteristics doubly confirms the presence of malignant microcalcification. Subsequently, by interpolating the physical characteristics via thresholding and mapping techniques, a three-dimensional (3D) projection of the region of interest (RoI) is obtained in terms of the distance in millimeter. The size of a microcalcification is determined using this 3D-projected view. This algorithm is verified with 100 abnormal mammogram images showing microcalcification and 10 normal mammogram images. In addition to the size calculation, the proposed algorithm acts as a good classifier that is used to classify the considered input image as normal or abnormal with the help of only two physical characteristics. This proposed algorithm exhibits a classification accuracy of 99%. [ABSTRACT FROM AUTHOR]

Background and objective Early detection is the important key to reduce breast cancer mortality rate. Detecting the mammographic abnormality as a subtle sign of breast cancer is essential for the proper diagnosis and treatment. The aim of this preliminary study is to develop algorithms which detect suspicious lesions and characterize them to reduce the diagnostic errors regarding false positives and false negatives. Methods The proposed hybrid mechanism detects suspicious lesions automatically using connected component labeling and adaptive fuzzy region growing algorithm. A novel neighboring pixel selection algorithm reduces the computational complexity of the seeded region growing algorithm used to finalize lesion contours. These lesions are characterized using radiomic features and then classified as benign mass or malignant tumor using k -NN and SVM classifiers. Two datasets of 460 full field digital mammograms (FFDM) utilized in this clinical study consists of 210 images with malignant tumors, 30 with benign masses and 220 normal breast images that are validated by radiologists expert in mammography. Results The qualitative assessment of segmentation results by the expert radiologists shows 91.67% sensitivity and 58.33% specificity. The effects of seven geometric and 48 textural features on classification accuracy, false positives per image (FPsI), sensitivity and specificity are studied separately and together. The features together achieved the sensitivity of 84.44% and 85.56%, specificity of 91.11% and 91.67% with FPsI of 0.54 and 0.55 using k -NN and SVM classifiers respectively on local dataset. Conclusions The overall breast cancer detection performance of proposed scheme after combining geometric and textural features with both classifiers is improved in terms of sensitivity, specificity, and FPsI. [ABSTRACT FROM AUTHOR]

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ( 95.00 ± 0.57 %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01 .

The image processing of mammograms is very important for the early detection of breast pathologies, including cancer. This paper proposes a new technique based on directional fractal filtering for detecting microcalcification clusters or irregularly shaped microcalcifications. The proposed algorithm has two parts: a preprocessing step for detecting and locating microcalcification; and a second zooming, enhancement, and segmentation step. Detection is performed by image convolution using a set of masks with interesting fractal properties. Combined with other simple mathematical operations, remarkable contrast enhancement and segmentation are produced. The final result permits the clear delineation of the shape of individual microcalcifications. A comparison is made with other microcalcification enhancement techniques described in the literature.

Content-based image retrieval (CBIR) shows great significance in clinical decision-making, which explores the visual content of medical images rather than keywords, tags, or descriptions. It provides doctors an image-guided approach to explore relevant cases that could offer doctors instructive reference. Mammogram screening has been known to be widely used in the early stage diagnosis of breast cancer and could reduce its morbidity and mortality. In this paper, we aim to develop a scalable CBIR method for a large repository of mammogram. To this end, we extend the original Anchor Graph Hashing (AGH) and propose a new unsupervised hashing algorithm, named as composite AGH with iterative quantization (C-AGH-ITQ), which compresses mammographic regions of interest (ROIs) into compact binary codes and enables real-time searching in Hamming space. Multimodal features and different distance metrics are integrated, performing upon a composite Anchor Graph. To improve the effectiveness of the hash code, quantization error is further iteratively minimized by introducing an orthogonal rotation matrix. We evaluate the presented C-AGH-ITQ algorithm on a data set of 11 533 mammographic ROIs obtained from the Digital Database for Screening Mammography. Our method obtains more than 84% retrieval precision and 93% classification accuracy (using $k$ NN prediction), which demonstrates that hash codes produced by C-AGH-ITQ well capture the visual similarities between mammographic images. In addition, since C-AGH-ITQ ensures linear complexity of the training procedure and constant time for query, our system is readily applicable to large-scale mammogram databases and has the potential to provide abundant clinical cases as reference. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: This study aimed to determine whether mammographic features assessed by radiologists and using computer algorithms are prognostic of occult invasive disease for patients showing ductal carcinoma in situ (DCIS) only in core biopsy.Materials and Methods: In this retrospective study, we analyzed data from 99 subjects with DCIS (74 pure DCIS, 25 DCIS with occult invasion). We developed a computer-vision algorithm capable of extracting 113 features from magnification views in mammograms and combining these features to predict whether a DCIS case will be upstaged to invasive cancer at the time of definitive surgery. In comparison, we also built predictive models based on physician-interpreted features, which included histologic features extracted from biopsy reports and Breast Imaging Reporting and Data System-related mammographic features assessed by two radiologists. The generalization performance was assessed using leave-one-out cross validation with the receiver operating characteristic curve analysis.Results: Using the computer-extracted mammographic features, the multivariate classifier was able to distinguish DCIS with occult invasion from pure DCIS, with an area under the curve for receiver operating characteristic equal to 0.70 (95% confidence interval: 0.59-0.81). The physician-interpreted features including histologic features and Breast Imaging Reporting and Data System-related mammographic features assessed by two radiologists showed mixed results, and only one radiologist's subjective assessment was predictive, with an area under the curve for receiver operating characteristic equal to 0.68 (95% confidence interval: 0.57-0.81).Conclusions: Predicting upstaging for DCIS based upon mammograms is challenging, and there exists significant interobserver variability among radiologists. However, the proposed computer-extracted mammographic features are promising for the prediction of occult invasion in DCIS. [ABSTRACT FROM AUTHOR]

Breast Cancer is reported to be one of the major causes of mortality among the women across the world. Digital mammogram is considered to be a reliable breast screening tool due to its low-dose of energy conservation. This paper aims at the detection and segmentation of breast masses using watershed segmentation. Further, this research work explores the statistical and texture features of the segmented masses. These features form the foundation for the classification and detection of breast cancer. The proposed method named as Feature Extraction using Statistical and Texture Measure (FESTM) is experimented on the Mammographic Image Analysis Society (MIAS) test datasets, for validation and verification. It is confirmed that the segmentation results align with the ground truth data of test images. [ABSTRACT FROM AUTHOR]

In this paper, I introduce a new method for feature extraction to classify digital mammograms using fast finite shearlet transform. Initially, fast finite shearlet transform was performed over mammogram images, and feature vectors were built using coefficients of the transform. In subsequent calculations, features were ranked according to t -test statistics, and capabilities were distinguished between different classes. To maximize differences between class representatives, a thresholding process was implemented as a final stage of feature extraction, and classifications were calculated over the optimal feature set using 5-fold cross validation and a support vector machine (SVM) classifier. The present results show that the proposed method provides satisfactory classification accuracy. [ABSTRACT FROM AUTHOR]

• A novel deep learning model, LRP-NET, is proposed to capture spatiotemporal breast tissue changes over longitudinal negative/benign mammogram examinations for breast cancer risk prediction. • The structure of LRP-NET is designed referring to clinical knowledge to capture bilateral breast tissue changes in relation to breast cancer risk assessment. • The LRP-NET model is compared to other related models and shows outperforming performance for near-term breast cancer risk prediction. Information in digital mammogram images has been shown to be associated with the risk of developing breast cancer. Longitudinal breast cancer screening mammogram examinations may carry spatiotemporal information that can enhance breast cancer risk prediction. No deep learning models have been designed to capture such spatiotemporal information over multiple examinations to predict the risk. In this study, we propose a novel deep learning structure, LRP-NET, to capture the spatiotemporal changes of breast tissue over multiple negative/benign screening mammogram examinations to predict near-term breast cancer risk in a case-control setting. Specifically, LRP-NET is designed based on clinical knowledge to capture the imaging changes of bilateral breast tissue over four sequential mammogram examinations. We evaluate our proposed model with two ablation studies and compare it to three models/settings, including 1) a "loose" model without explicitly capturing the spatiotemporal changes over longitudinal examinations, 2) LRP-NET but using a varying number (i.e., 1 and 3) of sequential examinations, and 3) a previous model that uses only a single mammogram examination. On a case-control cohort of 200 patients, each with four examinations, our experiments on a total of 3200 images show that the LRP-NET model outperforms the compared models/settings. [ABSTRACT FROM AUTHOR]

Removal of noise and pectoral muscles are the two important pre-processing steps in CAD system for the diagnosis of breast cancer. This work combines Robust Outlyingness Ratio (ROR) mechanism with extended NL-Means (ROR-NLM) filter based on Discrete Cosine Transform (DCT) for the detection and removal of noise. This method removes Gaussian and impulse noise very effectively without any loss of desired data. For segmenting and removing pectoral muscles, this paper uses global thresholding to identify pectoral muscles, edge detection processes to identify the edge of the full breast and connected component labelling to identify and remove the connected pixels outside the breast region. The result shows that our approach removes Gaussian and impulse noise effectively without any loss of desired data and overall gives 90.06% accuracy. [ABSTRACT FROM AUTHOR]

We present new geometric shape and margin features for classifying mammogram mass lesions into BI-RADS shape categories: round, oval, lobular and irregular. According to Breast Imaging Reporting and Data System (BIRADS), masses can be differentiated using its shape, size and density, which is how radiologist visualizes the mammograms. Measuring regular and irregular shapes mathematically is found to be a difficult task, since there is no single measure available to differentiate various shapes. It is known that for mammograms, shape features are superior to Haralick and wavelet based features. Various geometrical shape and margin features have been introduced based on maximum and minimum radius of mass to classify the morphology of masses. These geometric features are found to be good in discriminating regular shapes from irregular shapes. In this paper, each mass is described by shape feature vector consists of 17 shape and margin properties. The masses are classified into 4 categories such as round, oval, lobular and irregular. Classifying masses into 4 categories is a very difficult task compared to classifying masses as benign, malignant or normal vs. abnormal. Only shape and margin characteristics can be used to discriminate these 4 categories effectively. Experiments have been conducted on mammogram images from the Digital Database for Screening Mammography (DDSM) and classified using C5.0 decision tree classifier. Total of 224 DDSM mammogram masses are considered for experiment. The C5.0 decision tree algorithm is used to generate simple rules, which can be easily implemented and used in fuzzy inference system as ifythenyelse statements. The rules are used to construct the generalized fuzzy membership function for classifying the masses as round, oval, lobular or irregular. Proposed approach is twice effective than existing Beamlet based features for classifying the mass as round, oval, lobular or irregular. [ABSTRACT FROM AUTHOR]

Abstract: Breast cancer is one of the causes of death that often occurred to women. Computer Aided Diagnosis (CADx) is system to determine type of cancer. This system divided into two phases, feature extraction and classification. So far, Discrete Wavelet Transform (DWT) is frequently used for feature extraction. Unfortunately, DWT has severe shift dependence and poor directional selectivity in 2-D and 3-D. This paper mainly focused on mammogram classification using Dual Tree Complex Wavelet Transform (DT CWT) as feature extraction and Neural Network (NN) as classifier. Using this methodology produces accuracy 93.33% for cancer type classification. This accuracy is little higher than DWT. Data obtained from Mammographic Image Analysis Society (MIAS) database. [Copyright &y& Elsevier]

Abstract: This paper presents a method for breast cancer diagnosis in digital mammogram images. Multiresolution representations, wavelet or curvelet, are used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet or curvelet coefficients of each image in row vector, where the number of rows is the number of images, and the number of columns is the number of coefficients. A feature extraction method is developed based on the statistical t-test method. The method is ranking the features (columns) according to its capability to differentiate the classes. Then, a dynamic threshold is applied to optimize the number of features, which can achieve the maximum classification accuracy rate. The method depends on extracting the features that can maximize the ability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Support vector machine (SVM) is used to classify between the normal and abnormal tissues and to distinguish between benign and malignant tumors. The proposed method is validated using 5-fold cross validation. The obtained classification accuracy rates demonstrate that the proposed method could contribute to the successful detection of breast cancer. [Copyright &y& Elsevier]

This paper reports the design and implementation of an intelligent system for detection of microcalcification from digital mammograms. A neuron based thresholding strategy has been developed to reduce the number of candidate pixels. A back propagation neural network (BPNN) classifier has been used to classify the pixels into positive (affected) and normal ones. The false positives generated in the process are eliminated using the connected component analysis and the elongated component removal algorithms in succession. Suspected areas of microcalcification are detected and marked on the mammogram. The system was rigorously tested for the available images and was found to be quite robust, consistent and fast in detection. The output image with prompts generated by the system can form an important input to a radiologist for the final diagnosis. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms. [Copyright &y& Elsevier]

Abstract: This paper presents a comparative study between wavelet and curvelet transform for breast cancer diagnosis in digital mammogram. Using multiresolution analysis, mammogram images are decomposed into different resolution levels, which are sensitive to different frequency bands. A set of the biggest coefficients from each decomposition level is extracted. Then a supervised classifier system based on Euclidian distance is constructed. The performance of the classifier is evaluated using a 2×5-fold cross validation followed by a statistical analysis. The experimental results suggest that curvelet transform outperforms wavelet transform and the difference is statistically significant. [Copyright &y& Elsevier]

Specimen mammography during image guided breast surgery is a daily occurrence. The process of specimen travel, imaging and reporting may take 20–30 minutes. An intraoperative method to obtain digital specimen mammograms may expedite the process. We compared intraoperative digital specimen mammography (IDSM) as well as standard specimen mammography (SSM) on 121 consecutive image guided lumpectomies. Each lumpectomy specimen had IDSM obtained followed by travel to radiology for SSM. Surgical decisions were based on all imaging obtained. Data included 1) the ability of each imaging method to identify the target lesion, 2) degree of concordance of surgical interpretation of IDSM compared to radiologist interpretation of SSM, 3) the time required from lumpectomy to surgical review of images from each method, and 4) potential operative time savings. Intraoperative digital specimen mammography (IDSM) was equally as accurate as standard x-ray film specimen mammography. There was no significant difference between 1) the frequency of identification of the target lesion by surgeon or radiologist, 2) lack of identification of any lesion, or 3) frequency of involved margins using imaging criteria. However, there was a marked difference in 1) the time needed to obtain images ready to read, 2) the ability to re-excise tissue promptly, and 3) the overall operating room time with an average decrease of 19 minutes. Intraoperative digital specimen mammography (IDSM) was equally accurate as SSM obtained in this study. Use of this new technology allows surgeons to quickly view specimen images which translate into shorter more efficient operations. [ABSTRACT FROM AUTHOR]

Abstract: A multiresolution analysis system for interpreting digital mammograms is proposed and tested. This system is based on using fractional amount of biggest wavelets coefficients in multilevel decomposition. A set of real labeled database is used in evaluating the proposed system. The evaluation results show that the system has a remarkably high efficiency compared by other systems known till present especially in the area of distinguishing between benign and malignant tumors. [Copyright &y& Elsevier]

Introduction: To present the use of digital spot mammography (DSM) in a diagnostic practice. Methods and Patients: Digital spot images of 779 women requiring a spot compression or a spot magnification view were collected. The digital images were acquired on a digital spot upright unit using a 61 mm x 61 mm field of view. Lesions reported included masses, calcifications, and areas of distortions. Results: 1065 lesions required additional views with DSM. Lesions reported included masses (n = 113), masses and microcalcifications (n = 53), spiculated masses (n = 34), architectural distortions (n = 16), and microcalcifications (n = 849). DSMs were considered to be adequate in 97.7% of patients. Unsatisfactory exams resulted from difficulties encountered in targeting the area of interest at the beginning of our experience. Conclusion: DSM, most commonly used to perform interventional procedures, can also be used in a diagnostic practice taking advantage of post-processing of images not available with conventional spot compression and magnification. [ABSTRACT FROM AUTHOR]

Defining region of interests (ROIs) containing abnormal lesions on digital mammograms is the first step in many Computer-Aided-Diagnosis (CAD) systems for the classification of early signs of breast cancer as malignant or benign. The motivation of this paper is to study the efficiency of automated methods used in clustered microcalcifications (MCCs) detection module of a proposed CAD system. The proposed methods are based on several image processing concepts, such as morphological processing, fractal analysis, adaptive wavelet transform, local maxima detection and high-order statistics (HOS) tests. We applied these methods on a set of MIAS database mammograms. The mammograms consisted of two groups, which were cancerous (clustered MCCs) and non-cancerous (normal) and they were digitized at a size of 1024 by 1024 with 256 gray levels. The results showed that the efficiency of HOS test, fractal analysis and morphological approach were 99%, 92% and 74%, respectively. It was proven that the HOS test was the most efficient, and gave reliable results for every mammogram tested. [ABSTRACT FROM AUTHOR]

This study investigates the equivalence or compatibility between U-Net and visual segmentations of fibroglandular tissue regions by mammography experts for calculating the breast density and mean glandular dose (MGD). A total of 703 mediolateral oblique-view mammograms were used for segmentation. Two region types were set as the ground truth (determined visually): (1) one type included only the region where fibroglandular tissue was identifiable (called the 'dense region'); (2) the other type included the region where the fibroglandular tissue may have existed in the past, provided that apparent adipose-only parts, such as the retromammary space, are excluded (the 'diffuse region'). U-Net was trained to segment the fibroglandular tissue region with an adaptive moment estimation optimiser, five-fold cross-validated with 400 training and 100 validation mammograms, and tested with 203 mammograms. The breast density and MGD were calculated using the van Engeland and Dance formulas, respectively, and compared between U-Net and the ground truth with the Dice similarity coefficient and Bland-Altman analysis. Dice similarity coefficients between U-Net and the ground truth were 0.895 and 0.939 for the dense and diffuse regions, respectively. In the Bland-Altman analysis, no proportional or fixed errors were discovered in either the dense or diffuse region for breast density, whereas a slight proportional error was discovered in both regions for the MGD (the slopes of the regression lines were -0.0299 and -0.0443 for the dense and diffuse regions, respectively). Consequently, the U-Net and ground truth were deemed equivalent (interchangeable) for breast density and compatible (interchangeable following four simple arithmetic operations) for MGD. U-Net-based segmentation of the fibroglandular tissue region was satisfactory for both regions, providing reliable segmentation for breast density and MGD calculations. U-Net will be useful in developing a reliable individualised screening-mammography programme, instead of relying on the visual judgement of mammography experts., (© 2022 IOP Publishing Ltd.)

Over the past years, the surge in the necessity for early detection and diagnosis of breast cancer has resulted in many innovative research directions. According to the World Health Organization, an early and accurate detection of breast cancer successfully leads to a correct decision towards its treatment. Development of computer-aided diagnosis (CAD) system is considered to be a major stead in current research practice to abet medical practitioners in decision-making. This paper proposes an improved CAD framework to correctly classify the digital mammograms into normal or abnormal, and further, benign or malignant. The proposed scheme employs a block-based discrete wavelet packet transform (BDWPT) to extract the features, namely, the Shannon entropy, Tsallis entropy, Renyi entropy, and energy. Then, principal component analysis (PCA) technique is utilized to extract the discriminating features from the original feature vector. Subsequently, an optimized wrapper-based kernel extreme learning machine (KELM) using a weighted chaotic salp swarm algorithm (WC-SSA) is proposed as classifier to classify the digital mammograms. Since the efficacy of KELM algorithm depends on its two important parameters, namely, the penalty parameter and the kernel parameter, the prime objective of the proposed work is to obtain the optimized value of the aforementioned parameters as well as to select the most relevant features from the reduced feature vector simultaneously. The proposed scheme is evaluated on three publicly available standard datasets, namely, MIAS, DDSM, and BCDR to validate the efficacy of the proposed BDWPT+PCA+WC-SSA-KELM scheme. The performance of the proposed model is evaluated in terms of different metrics, namely, classification accuracy, sensitivity, specificity, area under curve (AUC), Matthew's correlation coefficient (MCC), and F-measure via a 5 × 5 stratified cross-validation approach. From the experimental results and their analysis, it is observed that for the normal–abnormal category, the proposed technique results in an accuracy of 99.62% and 99.92% for MIAS and DDSM, respectively, whereas in the case of benign–malignant classification, the proposed method yields an accuracy of 99.28%, 99.63%, 99.60% for MIAS, DDSM, and BCDR datasets, respectively. Further, it is also observed that the proposed WC-SSA-KELM scheme exhibits superior performance as compared to that of its counterparts. Additionally, two well-known statistical analyses, namely, ANOVA and Friedman tests are performed to demonstrate that the performance of the proposed scheme is significantly better than that of the other existing schemes. • The present scheme effectively classifies the digital mammograms in real time. • Block-based discrete wavelet packet transform performs the feature extraction. • Proposed WC-SSA does feature selection and hyper-parameter optimization in parallel. [ABSTRACT FROM AUTHOR]

Purpose : With three-dimensional (3-D) images displayed as stacks of 2-D images, radiologists rely more heavily on vision away from their fixation point to visually process information, guide eye movements, and detect abnormalities. Thus the ability to detect targets away from the fixation point, commonly characterized as the useful field of view (UFOV), becomes critical for these 3-D imaging modalities. We investigate how the UFOV, defined as the eccentricity, in which detection performance degrades to a given probability, varies across imaging modalities and targets. Approach : We measure the detectability of different targets at various distances from gaze locations for single slices of liver computed tomography (CT), 2-D digital mammograms (DM), and single slices of digital breast tomosynthesis (DBT) cases. Observers with varying expertise were instructed to maintain their gaze at a point while a short display of the image was flashed and an eye tracker verified observer's steady fixation. Display times were 200 and 1000 ms for CT images and 500 ms for DM and DBT images. Results : We find variations in the UFOV from 9 to 12 deg for liver CT to as small as 2.5 to 5 deg for calcification clusters in breast images (DM and DBT). We compare our results to those reported in the literature for lung nodules and discuss the differences across methods used to measure the UFOV, their dependence on case selection/task difficulty, viewing conditions, and observer expertise. We propose a complementary measure defined in terms of performance degradation relative to the peak foveal performance (relative-UFOV) to circumvent UFOV's variations with case selection/task difficulty. Conclusion : Our results highlight the variations in the UFOV across imaging modalities, target types, observer expertise, and measurement methods and suggest an additional relative-UFOV measure to more thoroughly characterize the detection performance away from point of fixation., (© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Background: The percentage of mammographic dense tissue (PD) defined by pixel value threshold is a well-established risk factor for breast cancer. Recently there has been some evidence to suggest that an increased threshold based on visual assessment could improve risk prediction. It is unknown, however, whether this also applies to volumetric density using digital raw mammograms.Method: Two case-control studies nested within a screening cohort (ages of participants 46-73 years) from Manchester UK were used. In the first study (317 cases and 947 controls) cases were detected at the first screen; whereas in the second study (318 cases and 935 controls), cases were diagnosed after the initial mammogram. Volpara software was used to estimate dense tissue height at each pixel point, and from these, volumetric and area-based PD were computed at a range of thresholds. Volumetric and area-based PDs were evaluated using conditional logistic regression, and their predictive ability was assessed using the Akaike information criterion (AIC) and matched concordance index (mC).Results: The best performing volumetric PD was based on a threshold of 5 mm of dense tissue height (which we refer to as VPD5), and the best areal PD was at a threshold level of 6 mm (which we refer to as APD6), using pooled data and in both studies separately. VPD5 showed a modest improvement in prediction performance compared to the original volumetric PD by Volpara with ΔAIC = 5.90 for the pooled data. APD6, on the other hand, shows much stronger evidence for better prediction performance, with ΔAIC = 14.52 for the pooled data, and mC increased slightly from 0.567 to 0.577.Conclusion: These results suggest that imposing a 5 mm threshold on dense tissue height for volumetric PD could result in better prediction of cancer risk. There is stronger evidence that area-based density with a 6 mm threshold gives better prediction than the original volumetric density metric. [ABSTRACT FROM AUTHOR]

Background: This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques., Discussion: The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized., Conclusion: The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Accurate and automatic segmentation of the pectoralis muscle is essential in many breast image processing procedures, for example, in the computation of volumetric breast density from digital mammograms. Its segmentation is a difficult task due to the heterogeneity of the region, neighborhood complexities, and shape variability. The segmentation is achieved by pixel classification through a Markov random field (MRF) image model. Using the image intensity feature as observable data and local spatial information as a priori, the posterior distribution is estimated in a stochastic process. With a variable potential component in the energy function, by the maximum a posteriori (MAP) estimate of the labeling image, given the image intensity feature which is assumed to follow a Gaussian distribution, we achieved convergence properties in an appropriate sense by Metropolis sampling the posterior distribution of the selected energy function. By proposing an adjustable spatial constraint, the MRF-MAP model is able to embody the shape requirement and provide the required flexibility for the model parameter fitting process. We demonstrate that accurate and robust segmentation can be achieved for the curving-triangle-shaped pectoralis muscle in the medio-lateral-oblique (MLO) view, and the semielliptic-shaped muscle in cranio-caudal (CC) view digital mammograms. The applicable mammograms can be either "For Processing" or "For Presentation" image formats. The algorithm was developed using 56 MLO-view and 79 CC-view FFDM "For Processing" images, and quantitatively evaluated against a random selection of 122 MLO-view and 173 CC-view FFDM images of both presentation intent types.