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IFCM is a variant of the conventional fuzzy\n                    <jats:italic>C<\/jats:italic>\n                    -means (FCM) based on intuitionistic fuzzy set (IFS) theory. Unlike FCM, IFCM considers both membership and nonmembership values. The existing IFCM method uses Sugeno\u2019s and Yager\u2019s IFS generators to compute nonmembership value. But for certain parameters, IFS constructed using above complement generators does not satisfy the elementary condition of intuitionism. To overcome this problem, this paper adopts a new IFS generator. Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. Extensive experimentations are carried out on standard datasets like brain, lungs, liver and breast images. This paper compares the proposed method with other IFS based methods. The proposed algorithm satisfies the elementary condition of intuitionism. Further, this algorithm outperforms other methods with the use of various cluster validity functions.\n                  <\/jats:p>","DOI":"10.1515\/jisys-2016-0241","type":"journal-article","created":{"date-parts":[[2017,4,29]],"date-time":"2017-04-29T06:00:29Z","timestamp":1493445629000},"page":"593-607","source":"Crossref","is-referenced-by-count":23,"title":["A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation"],"prefix":"10.1515","volume":"27","author":[{"given":"S.V.","family":"Aruna Kumar","sequence":"first","affiliation":[{"name":"Department of Information Science and Engineering , Sri Jayachamarajendra College of Engineering , Mysuru , India"}]},{"given":"B.S.","family":"Harish","sequence":"additional","affiliation":[{"name":"Department of Information Science and Engineering , Sri Jayachamarajendra College of Engineering , Mysuru , India"}]}],"member":"374","published-online":{"date-parts":[[2017,4,29]]},"reference":[{"key":"2025120523275885308_j_jisys-2016-0241_ref_001_w2aab3b7b7b1b6b1ab1b5b1Aa","doi-asserted-by":"crossref","unstructured":"M. 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