A segmentation approach for mammographic images and its clinical value

© 2017 IEEE. We developed the nested contour algorithm (NCA) - a segmentation method for X-ray mammography images and tested it on a set of 1532 images of 356 women with morphologically proven breast cancer of various characteristics located on different density background. As a result NCA correctly marked 48 of 52 (92.31 %) star-like lesions, 12 of 14 (85.71 %) architectural distortions, 51 of 58 (87.93 %) lesions with irregular shape and unclear margin, all 18 lobular and round lesions, 17 of 18 (94.4 %) partially visualized lesions, 13 of 18 (72.2 %) asymmetric areas and 7 of 16 (43.8 %) unclearly visible or invisible lesions. Overall sensitivity of NCA in our set was 90.73 % (323 of 356 cases). The mean rate of false-positive marks was 1.3 per image - for ACR A-B mammograms and 1.8 - for ACR C-D mammograms