Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering
Joint Authors
Zhu, Hong
Fang, Qianhao
He, Hanzhi
Xu, Jinhui
Wang, Wei
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-24
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In this paper, we propose a novel algorithm for medical image segmentation, which combines the density peaks clustering (DPC) with the fruit fly optimization algorithm, and it has the following advantages.
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.
American Psychological Association (APA)
Zhu, Hong& He, Hanzhi& Xu, Jinhui& Fang, Qianhao& Wang, Wei. 2018. Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1131891
Modern Language Association (MLA)
Zhu, Hong…[et al.]. Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1131891
American Medical Association (AMA)
Zhu, Hong& He, Hanzhi& Xu, Jinhui& Fang, Qianhao& Wang, Wei. Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1131891
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131891