Fuzzy c-means clustering with weighted image patch for image segmentation |
| |
| Authors: | Zexuan Ji Yong Xia Qiang Chen Quansen Sun Deshen Xia David Dagan Feng |
| |
| Affiliation: | 1. Nanjing University of Science and Technology School of Computer Science and Technology, Nanjing 210094, China;2. Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, NSW 2006, Australia;3. School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China;4. Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200025, China;1. School of Management Science and Engineering, Dongbei University of Finance & Economics, Jianshan Street 217, Dalian 116025, PR China;2. Graduate School of Management, Clark University, 950 Main Street, Worcester, MA 01610-1477, USA;3. School of Business, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280, USA;1. Department of Mathematics, Bareilly College, Bareilly, Uttar Pradesh 243005, India;2. Computer Science & Engineering Department, Institute of Engineering & Technology, Lucknow, Uttar Pradesh 226021, India;3. Faculty of Engineering & Technology, Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh 226031, India |
| |
| Abstract: | Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide-spread prevalence, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, we propose the weighted image patch-based FCM (WIPFCM) algorithm for image segmentation. In this algorithm, we use image patches to replace pixels in the fuzzy clustering, and construct a weighting scheme to able the pixels in each image patch to have anisotropic weights. Thus, the proposed algorithm incorporates local spatial information embedded in the image into the segmentation process, and hence improve its robustness to noise. We compared the novel algorithm to several state-of-the-art segmentation approaches in synthetic images and clinical brain MR studies. Our results show that the proposed WIPFCM algorithm can effectively overcome the impact of noise and substantially improve the accuracy of image segmentations. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
