{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T20:30:46Z","timestamp":1791232246788,"version":"4.1.0"},"reference-count":18,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2004,4,6]],"date-time":"2004-04-06T00:00:00Z","timestamp":1081209600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2004,5]]},"abstract":"<jats:p>Mass segmentation plays a crucial role in computer\u2010aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign, or malignant. In this article we present a robust and automated segmentation technique\u2014based on dynamic programming\u2014to segment mass lesions from surrounding tissue. In addition, we propose an efficient algorithm to guarantee resulting contours to be closed. The segmentation method based on dynamic programming was quantitatively compared with two other automated segmentation methods (region growing and the discrete contour model) on a dataset of 1210 masses. For each mass an overlap criterion was calculated to determine the similarity with manual segmentation. The mean overlap percentage for dynamic programming was 0.69, for the other two methods 0.60 and 0.59, respectively. The difference in overlap percentage was statistically significant. To study the influence of the segmentation method on the performance of a CAD system two additional experiments were carried out. The first experiment studied the detection performance of the CAD system for the different segmentation methods. Free\u2010response receiver operating characteristics analysis showed that the detection performance was nearly identical for the three segmentation methods. In the second experiment the ability of the classifier to discriminate between malignant and benign lesions was studied. For region based evaluation the area<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/mp8039-math-0001.png\" xlink:title=\"urn:x-wiley:00942405:media:mp8039:mp8039-math-0001\"\/>under the receiver operating characteristics curve was 0.74 for dynamic programming, 0.72 for the discrete contour model, and 0.67 for region growing. The difference in<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/mp8039-math-0002.png\" xlink:title=\"urn:x-wiley:00942405:media:mp8039:mp8039-math-0002\"\/>values obtained by the dynamic programming method and region growing was statistically significant. The differences between other methods were not significant.<\/jats:p>","DOI":"10.1118\/1.1688039","type":"journal-article","created":{"date-parts":[[2004,5,5]],"date-time":"2004-05-05T22:03:05Z","timestamp":1083794585000},"page":"958-971","source":"Crossref","is-referenced-by-count":132,"title":["A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography"],"prefix":"10.1002","volume":"31","author":[{"given":"Sheila","family":"Timp","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nico","family":"Karssemeijer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2004,4,6]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2271011962"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1076-6332(99)80058-0"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.212.3.r99au47817"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.730396"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"D. Guliato R. M. Rangayyan W. A. Carnielli J. A. Zuffo and J. E. L. Desautels \u201cSegmentation of breast tumors in mammograms by fuzzy region growing \u201d inProc. 20th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society 1998 pp. 1002\u20131004.","DOI":"10.1109\/IEMBS.1998.745618"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"D. Guliato R. M. Rangayyan J. A. Zuffo and J. E. Leo Desautels \u201cDetection of breast tumor boundaries using iso\u2010intensity contours and dynamic thresholding \u201d inDigital Mammography edited by N. Karssemeijer M. A. O. Thijssen J. H. C. L. Hendriks and L. J. T. O. van Erning (Kluwer Dordrecht 1998) pp. 253\u2013260.","DOI":"10.1007\/978-94-011-5318-8_42"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.598658"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.370398"},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","unstructured":"G. M.te Brake M. J.Stoutjesdijk andN.Karssemeijer \u201cA discrete dynamic countour model for mass segmentation in digital mammograms \u201d SPIE Medical Imaging3661 911\u2013919 (1999).","DOI":"10.1117\/12.348651"},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"S. Timp N. Karssemeijer and J. H. C. L. Hendriks \u201cComparison of three different mass segmentation methods \u201d inDigital Mammography IWDM 2002 edited by H. Peitgen (Springer\u2010Verlag Berlin 2003).","DOI":"10.1007\/978-3-642-59327-7_52"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.974922"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"S. Timp N. Karssemeijer and J. H. C. L. Hendriks \u201cAnalysis of changes in masses using contrast and size measures \u201d inDigital Mammography IWDM 2002 edited by H. Peitgen (Springer\u2010Verlag Berlin 2003).","DOI":"10.1007\/978-3-642-59327-7_58"},{"key":"e_1_2_1_14_1","unstructured":"E. Claridge and J. H. Richter \u201cCharacterization of mammographic lesions \u201d inDigital Mammography edited by A. G. Gale et al. (Elsevier Amsterdam 1994) pp. 241\u2013250."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1361-8415(96)80007-7"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.790462"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.538938"},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","unstructured":"C. Varela N. Karssemeijer J. M. Muller and P. G. Tahoces \u201cClassification of breast tumors in digitized mammograms \u201d inDigital Mammography IWDM 2002 edited by H. 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