{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:49:47Z","timestamp":1747190987874,"version":"3.40.5"},"reference-count":27,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Medical and Health Plan","award":["2018KY923","2016zdyf02"],"award-info":[{"award-number":["2018KY923","2016zdyf02"]}]},{"DOI":"10.13039\/100018927","name":"Lishui Science and Technology Bureau","doi-asserted-by":"crossref","award":["2018KY923","2016zdyf02"],"award-info":[{"award-number":["2018KY923","2016zdyf02"]}],"id":[{"id":"10.13039\/100018927","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2022,7,5]]},"abstract":"<jats:p>This study was aimed to explore the application of fuzzy C-means (FCM) algorithm in MR images of acquired immune deficiency syndrome (AIDS) patients. Sixty AIDS patients with central nervous disease were selected as the research object. A method of brain MR image segmentation based on FCM clustering optimization was proposed, and FCM was optimized based on the neighborhood pixel correlation of gray difference. The correlation was introduced into the objective function to obtain more accurate pixel membership and segmentation features of the image. The segmented image can retain the original image information. The proposed algorithm can clearly distinguish gray matter from white matter in images. The average time of image segmentation was 0.142\u2009s, the longest time of level set algorithm was 2.887\u2009s, and the running time of multithreshold algorithm was 1.708\u2009s. FCM algorithm had the shortest running time, and the average time was significantly better than other algorithms (<jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>P<\/a:mi>\n                        <a:mo>&lt;<\/a:mo>\n                        <a:mn>0.05<\/a:mn>\n                     <\/a:math>\n                  <\/jats:inline-formula>). FCM image segmentation efficiency was above 90%, and patients can clearly display the location of lesions after MRI imaging examination. In summary, FCM algorithm can effectively combine the spatial neighborhood information of the brain image, segment the BRAIN MR image, analyze the characteristics of AIDS patients from different directions, and provide effective treatment for patients.<\/jats:p>","DOI":"10.1155\/2022\/4955555","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T02:05:05Z","timestamp":1657073105000},"page":"1-9","source":"Crossref","is-referenced-by-count":0,"title":["Magnetic Resonance Features of Acquired Immune Deficiency Syndrome Involving Central Nervous System Diseases by Intelligent Fuzzy C-Means Clustering (FCM) Algorithm"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5894-0236","authenticated-orcid":true,"given":"Gang","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Chinese Medicine, Lishui People\u2019s Hospital, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui City 323000, 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