Tezture Segmentation Based on Permutation Entropy

摘要：

A new method based on permutation entropy and grey level feature is provided in this paper. Permutation entropy is a new complexity measure for time series based on comparison of neighbouring values. The definition applies to describe the texture feature of image. The new complexity measure feature combines with the grey-scale mean and grey-scale deviation, construct multi-dimension feature vector. Then, apply the fuzzy c-means algorithm as the classifier to cluster the feature vectors, get the texture segmentation results. Experiments show that the method is particularly useful in the presence of dynamical or observational noise and the advantages of the method are its simplicity, extremely fast calculation, its robustness.

关键词： Tezture Segmentation Permutation Entropy Gray feature Fuzzy c-Means

作者: Yi Li Cheng Qian

作者单位: Institute for Biomedical Engineering and Instrument Hangzhou Dianzi University Hangzhou,Zhejiang Province,China

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）