{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T20:31:53Z","timestamp":1791232313620,"version":"4.1.0"},"reference-count":28,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2010,10,28]],"date-time":"2010-10-28T00:00:00Z","timestamp":1288224000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"NIH\/NCI","award":["R33CAI02960"],"award-info":[{"award-number":["R33CAI02960"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2010,11]]},"abstract":"<jats:sec><jats:title>Purpose:<\/jats:title><jats:p>A learning\u2010based approach integrating the use of pixel\u2010level statistical modeling and spiculation detection is presented for the segmentation of mammographic masses with ill\u2010defined margins and spiculations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p>The algorithm involves a multiphase pixel\u2010level classification, using a comprehensive group of features computed from regional intensity, shape, and textures, to generate a mass\u2010conditional probability map (PM). Then, the mass candidate, along with the background clutters consisting of breast fibroglandular and other nonmass tissues, is extracted from the PM by integrating the prior knowledge of shape and location of masses. A multiscale steerable ridge detection algorithm is employed to detect spiculations. Finally, all the object\u2010level findings, including mass candidate, detected spiculations, and clutters, along with the PM, are integrated by graph cuts to generate the final segmentation mask.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p>The method was tested on 54 masses (51 malignant and 3 benign), all with ill\u2010defined margins and irregular shape or spiculations. The ground truth delineations were provided by five experienced radiologists. Area overlapping ratio of 0.689 (\u00b10.160) and 0.540 (\u00b10.164) were obtained for segmenting entire mass and margin portion only, respectively. Williams index of area and contour based measurements indicated that the segmentation results of the algorithm agreed well with the radiologists' delineation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions:<\/jats:title><jats:p>The proposed approach could closely delineate the mass body. Most importantly, it is capable of including mass margin and its spicule extensions which are considered as key features for breast lesion analyses.<\/jats:p><\/jats:sec>","DOI":"10.1118\/1.3490477","type":"journal-article","created":{"date-parts":[[2010,11,2]],"date-time":"2010-11-02T13:04:46Z","timestamp":1288703086000},"page":"5993-6002","source":"Crossref","is-referenced-by-count":15,"title":["Multilevel learning\u2010based segmentation of ill\u2010defined and spiculated masses in mammograms"],"prefix":"10.1002","volume":"37","author":[{"given":"Yimo","family":"Tao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shih\u2010Chung B.","family":"Lo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew T.","family":"Freedman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erini","family":"Makariou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Xuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2010,10,28]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.597707"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.730396"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1781551"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1339884"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.921478"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1381548"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2820630"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2791034"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3121511"},{"key":"e_1_2_7_11_1","series-title":"Atlas of Mammography: Histologic and Mammographic Correlations","author":"Martin J. 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