DocumentCode :
2523475
Title :
Texture Segmentation Based on Permutation Entropy
Author :
Li Yi ; Fan, Yingle ; Qian Cheng
Author_Institution :
Inst. for Biomed. Eng. & Instrum., Hangzhou Dianzi Univ., Hangzhou, China
fYear :
2009
fDate :
11-13 June 2009
Firstpage :
1
Lastpage :
4
Abstract :
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.
Keywords :
entropy; feature extraction; fuzzy set theory; image classification; image segmentation; image texture; pattern clustering; feature vector clustering; fuzzy c-means algorithm; grey level feature; grey-scale deviation; grey-scale mean; image classifier; image texture; multidimension feature vector; permutation entropy; texture segmentation; time series; Biomedical engineering; Biomedical measurements; Discrete wavelet transforms; Entropy; Frequency estimation; Image processing; Image segmentation; Noise robustness; Stochastic processes; Time frequency analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2901-1
Electronic_ISBN :
978-1-4244-2902-8
Type :
conf
DOI :
10.1109/ICBBE.2009.5163567
Filename :
5163567
Link To Document :
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