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A segmentation approach for mammographic images and its clinical value

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dc.date.accessioned 2019-01-22T20:53:18Z
dc.date.available 2019-01-22T20:53:18Z
dc.date.issued 2018
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/149268
dc.description.abstract © 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.
dc.subject Breast cancer
dc.subject Computer-aided detection
dc.subject Image analysis
dc.subject Mammography
dc.subject Segmentation
dc.title A segmentation approach for mammographic images and its clinical value
dc.type Conference Paper
dc.relation.ispartofseries-volume 2017-November
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 1
dc.source.id SCOPUS-2018-2017-SID85045837965


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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