期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2015
卷号：8
期号：3
页码：251-260
DOI：10.14257/ijsip.2015.8.3.23
出版社：SERSC
摘要：In this paper, a novel image segmentation algorithm based on fuzzy clustering and entropy analysis using space information for optical images is proposed. We adopt the general properties of Hopfield neural network (HNN) and multi-synapse neural network (MSNN) to gain the center of the clusters and the fuzzy membership degrees for solving the optimization problems. As far as the noise influence is concerned, we introduce a novel window to improve the robustness of the proposed algorithm. In the experimental analysis part, we compare our method with some state-of-the-art methodologies and adopt the well-known test image databases to conduct the experiment. The result indicates that compared to FCM and some other clustering methods, our entropy and neural network based algorithm performs better. Our approach is less time-consuming and more robust to noise
