Image Segmentation Based on Fast Kernelized Fuzzy Clustering Analysis

摘要：

Based on kernelized fuzzy clustering analysis, this paper presents a fast image segmentation algorithm using a speeding-up scheme called reduced set representation. The proposed clustering algorithm has lower computational complexity and could be regarded as the generalized version of the traditional KFCM-I and KFCM-II algorithms. Moreover, an image intensity correction is employed during image segmentation process. With another speeding-up scheme called preclassification, the proposed intensity correction could further acclerate image segmentation. Experiments of MRI image segmentation have shown the effectiveness of the proposed algorithm, which outperforms in its rivals.

关键词： image segmentation kernelized clustering speed-up scheme image intensity correction

作者: Liang Liao Xu Shen Yanning Zhang

作者单位: Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, Northwestern Polytechn School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 4500 Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, Northwestern Polytechn

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 441-445

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）