{"id":1170,"date":"2026-01-25T19:28:34","date_gmt":"2026-01-25T19:28:34","guid":{"rendered":"https:\/\/www.cs.ubbcluj.ro\/~meco\/an-unsupervised-threshold-based-growcut-algorithm-for-mammography-lesion-detection-2022\/"},"modified":"2026-02-01T12:08:35","modified_gmt":"2026-02-01T12:08:35","slug":"an-unsupervised-threshold-based-growcut-algorithm-for-mammography-lesion-detection-2022","status":"publish","type":"post","link":"https:\/\/www.cs.ubbcluj.ro\/~meco\/an-unsupervised-threshold-based-growcut-algorithm-for-mammography-lesion-detection-2022\/","title":{"rendered":"An Unsupervised Threshold-based GrowCut Algorithm for Mammography Lesion Detection (2022)"},"content":{"rendered":"<div class=\"entry-content\">\n<p>International Conference on Knowledge-Based Intelligent Information &amp; Engineering Systems<\/p>\n<h2>Authors<\/h2>\n<p>Cristiana Moroz-Dubenco, Ad\u00e9l Bajcsi, A. Andreica, C. Chira<\/p>\n<h2>Abstract<\/h2>\n<p>Breast cancer causes numerous deaths worldwide; yet the numbers have decreased in the past years as a result of computer-aided diagnosis and proper treatment. The current paper is addressed to the base of such diagnosis system: pre-processing and segmentation. After a robust pre-processing, an unsupervised version of GrowCut is applied to define the location of the abnormality. We present a method to automatically define the foreground seeds used in GrowCut. For experiments, mammograms from mini-MIAS dataset are used and a precision of 93.63% for the foreground seeds masks is achieved, which leads to promising segmentation results.<\/p>\n<h2>Citation<\/h2>\n<pre class=\"wp-block-preformatted\">@Inproceedings{Moroz-Dubenco2022AnUT,\n author = {Cristiana Moroz-Dubenco and Ad\u00e9l Bajcsi and A. Andreica and C. Chira},\n booktitle = {International Conference on Knowledge-Based Intelligent Information &amp; Engineering Systems},\n title = {An Unsupervised Threshold-based GrowCut Algorithm for Mammography Lesion Detection},\n year = {2022}\n}<\/pre>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Breast cancer causes numerous deaths worldwide; yet the numbers have decreased in the past years as a result of computer-aided diagnosis and proper treatment. The current paper is addressed to the base of such diagnosis system: pre-processing and segmentation. After a robust pre-processing, an unsupervised version of GrowCut is applied to define the location of the abnormality. We present a method to automatically define the foreground seeds used in GrowCut. For experiments, mammograms from mini-MIAS dataset are used and a precision of 93.63% for the foreground seeds masks is achieved, which leads to promising segmentation results.<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":[],"categories":[4],"tags":[45,9,44,11,46],"_links":{"self":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1170"}],"collection":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/comments?post=1170"}],"version-history":[{"count":1,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1170\/revisions"}],"predecessor-version":[{"id":1506,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1170\/revisions\/1506"}],"wp:attachment":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/media?parent=1170"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/categories?post=1170"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/tags?post=1170"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}