Texture descriptors applied to digital mammography

Breast cancer is the second cause of death among women cancers. Computer Aided Detection has been demon- strated an useful tool for early diagnosis, a crucial as- pect for a high survival rate. In this context, several re- search works have incorporated texture features in mam- mographic image segmentation and description such as Gray-Level co-occurrence matrices, Local Binary Pat- terns, and many others. This paper presents an approach for breast density classi¯cation based on segmentation and texture feature extraction techniques in order to clas- sify digital mammograms according to their internal tis- sue. The aim of this work is to compare di®erent texture descriptors on the same framework (same algorithms for segmentation and classi¯cation, as well as same images). Extensive results prove the feasibility of the proposed ap- proach.Postprint (published version

Breast skin-line detection using dynamic programming

In this paper, we present a novel method to extract the breast skin-line based on dynamic programming. Skin-line extraction is an important preprocessing step in CAD systems; however, it is a challenging problem due to the presence of noise, underexposed regions, which results in a low contrast area near the skin-air interface, and artifacts such as labels. Our proposal utilizes the stroma edge to constrain searching for the border. In order to cope with noise, we consider several candidate points for the border interface which are obtained by the Laplace operator applied in pre-defined directions in the mammogram. The breast contour is obtained from the candidate points using a dynamic programming algorithm. This utilizes a criterion of optimality to obtain the optimum contour by minimization of a cost function. The method was evaluated using 82 mammograms whose contour were manually extracted by a radiologist from the mini- MIAS database. The Polyline Distance Measure was evaluated for each contour selected with the proposed method, obtaining a mean error of 2.05 pixels and a standard deviation of 0.80.Fundação para a Ciência e a Tecnologia (FCT

Fully automated breast boundary and pectoral muscle segmentation in mammograms

Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in Computer Aided Diagnosis (CAD) systems. Estimating the breast and pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast boundary and pectoral muscle segmentation method in mammograms is proposed. For breast boundary estimation, we determine the initial breast boundary via thresholding and employ Active Contour Models without edges to search for the actual boundary. A post-processing technique is proposed to correct the overestimated boundary caused by artifacts. The pectoral muscle boundary is estimated using Canny edge detection and a pre-processing technique is proposed to remove noisy edges. Subsequently, we identify five edge features to find the edge that has the highest probability of being the initial pectoral contour and search for the actual boundary via contour growing. The segmentation results for the proposed method are compared with manual segmentations using 322, 208 and 100 mammograms from the Mammographic Image Analysis Society (MIAS), INBreast and Breast Cancer Digital Repository (BCDR) databases, respectively. Experimental results show that the breast boundary and pectoral muscle estimation methods achieved dice similarity coefficients of 98.8% and 97.8% (MIAS), 98.9% and 89.6% (INBreast) and 99.2% and 91.9% (BCDR), respectively

Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134976/1/mp8512.pd