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Simultaneously, this study improves the traditional clustering method by combining medical image feature diagnosis requirements. In addition, this study carried out image data processing through simulation, and designed comparative experiments to analyze the performance of the algorithm. The research shows that the FRFCM combined with the intuitionistic fuzzy set proposed in this paper has greatly improved the noise immunity and segmentation performance compared with the FCM based fuzzy set.<\/jats:p>","DOI":"10.3233\/jifs-189327","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T12:57:45Z","timestamp":1601038665000},"page":"2871-2879","source":"Crossref","is-referenced-by-count":2,"title":["FRFCM clustering segmentation method for\u00a0medical MR image feature diagnosis"],"prefix":"10.1177","volume":"40","author":[{"given":"Qian","family":"He","sequence":"first","affiliation":[{"name":"Department of Radiology, The Second People\u2019s Hospital of Yunnan Province, Kunming, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juwei","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second People\u2019s Hospital of Yunnan Province, Kunming, PR 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