TY - JOUR A2 - Winkler, David A. AU - Zhu, Hong AU - He, Hanzhi AU - Xu, Jinhui AU - Fang, Qianhao AU - Wang, Wei PY - 2018 DA - 2018/12/24 TI - Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering SP - 3052852 VL - 2018 AB - 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. SN - 1748-670X UR - https://doi.org/10.1155/2018/3052852 DO - 10.1155/2018/3052852 JF - Computational and Mathematical Methods in Medicine PB - Hindawi KW - ER -