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Firstly, it avoids the problem of DPC that needs to artificially select parameters (such as the number of clusters) in its decision graph and thus can automatically determine their values. Secondly, our algorithm uses random step size, instead of the fixed step size as in the fruit fly optimization algorithm, which helps avoid falling into local optima. Thirdly, our algorithm selects the cut-off distance and the cluster centers using the image entropy value and can better capture the structures of the image. Experiments on benchmark dataset and proprietary dataset show that our algorithm can adaptively segment medical images with faster convergence and better robustness.<\/jats:p>","DOI":"10.1155\/2018\/3052852","type":"journal-article","created":{"date-parts":[[2018,12,24]],"date-time":"2018-12-24T18:31:38Z","timestamp":1545676298000},"page":"1-11","source":"Crossref","is-referenced-by-count":12,"title":["Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1676-940X","authenticated-orcid":true,"given":"Hong","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Medical Information, Xuzhou Medical University, Xuzhou, China"},{"name":"Key Laboratory of Intelligent Industrial Control Technology of Jiangsu Province, College of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, China"},{"name":"Department of Computer Science and 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