DocumentCode :
3318356
Title :
Fuzzy-C-Means Clustering Based On The Gray And Spatial Feature For Image Segmentation
Author :
Li, Ming ; Li, Yun-song
Author_Institution :
Sch. of Comput. & Commun., Lanzhou Univ. of Technol.
Volume :
2
fYear :
2006
fDate :
3-6 Nov. 2006
Firstpage :
1641
Lastpage :
1646
Abstract :
Fuzzy c-means (FCM) clustering algorithm has been widely used in automated image segmentation. However, the standard FCM algorithm is sensitive to noise because of taking no into account the gray and spatial information of pixel. The paper proposes an improved FCM algorithm for image segmentation. We use the degree of gray similarity and distribution statistics of the neighbor pixels to form a new membership function for clustering. Not only it is effective to remove the noise spots and reduce the spurious blobs, but also it is ease to correct the misclassified pixels. Experimental results on three types of image indicate that the propose algorithm is more accurate and robust than the standard FCM algorithm
Keywords :
feature extraction; fuzzy set theory; image denoising; image segmentation; pattern clustering; statistics; distribution statistics; fuzzy-c-means clustering; gray feature; gray similarity; image segmentation; membership function; noise spots; spatial feature; spurious blob reduction; Clustering algorithms; Cost function; Image processing; Image segmentation; Iterative algorithms; Noise reduction; Noise robustness; Partitioning algorithms; Pixel; Statistical distributions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Security, 2006 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
1-4244-0605-6
Electronic_ISBN :
1-4244-0605-6
Type :
conf
DOI :
10.1109/ICCIAS.2006.295340
Filename :
4076246
Link To Document :
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