{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:39:51Z","timestamp":1783010391145,"version":"3.54.6"},"reference-count":53,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2017,7,26]],"date-time":"2017-07-26T00:00:00Z","timestamp":1501027200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Numer Methods Biomed Eng"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Automatic segmentation of abnormal region is a crucial task in computer\u2010aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation\u2013based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh\u2010free\u2013based radial basis function (RBF). The limitation of mesh\u2010based approach is removed by using mesh\u2010free\u2013based RBF method. The evolution of variational level set function is also done by mesh\u2010based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c\u2010means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function.<\/jats:p>","DOI":"10.1002\/cnm.2907","type":"journal-article","created":{"date-parts":[[2017,6,12]],"date-time":"2017-06-12T03:46:35Z","timestamp":1497239195000},"source":"Crossref","is-referenced-by-count":21,"title":["Mesh\u2010free based variational level set evolution for breast region segmentation and abnormality detection using mammograms"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1730-3617","authenticated-orcid":false,"given":"Kanchan L.","family":"Kashyap","sequence":"first","affiliation":[{"name":"Computer Science &amp; Engineering Indian Institute of Information Technology, Design &amp; Manufacturing Jabalpur Jabalpur India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manish K.","family":"Bajpai","sequence":"additional","affiliation":[{"name":"Computer Science &amp; Engineering Indian Institute of Information Technology, Design &amp; Manufacturing Jabalpur Jabalpur India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pritee","family":"Khanna","sequence":"additional","affiliation":[{"name":"Computer Science &amp; Engineering Indian Institute of Information Technology, Design &amp; Manufacturing Jabalpur Jabalpur India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Giakos","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Manhattan College Riverdale New York City NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,7,26]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2009.2033269"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.598228"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.887618"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.774164"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/79.814646"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2006.878003"},{"key":"e_1_2_8_8_1","doi-asserted-by":"crossref","unstructured":"CaoA SongQ YangX andWangL. 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