Title :
Image segmentation using probabilistic fuzzy c-means clustering
Author_Institution :
Analog Design Autom. Inc, Ottawa, Ont., Canada
fDate :
6/23/1905 12:00:00 AM
Abstract :
A new approach for gray-level image segmentation is presented using a probabilistic fuzzy c-means clustering algorithm. This approach combines the spatial probabilistic information and the fuzzy membership function in the clustering process. The proposed probabilistic fuzzy c-means method can deal effectively with image segmentation in a noisy environment
Keywords :
Gaussian noise; fuzzy set theory; image segmentation; pattern clustering; probability; white noise; Gaussian white noise; clustering process; fuzzy membership function; gray-level image segmentation; noisy environment; probabilistic fuzzy c-means clustering algorithm; spatial probabilistic information; Clustering algorithms; Design automation; Equations; Image edge detection; Image segmentation; Iterative algorithms; Merging; Pixel; Virtual colonoscopy; Working environment noise;
Conference_Titel :
Image Processing, 2001. Proceedings. 2001 International Conference on
Conference_Location :
Thessaloniki
Print_ISBN :
0-7803-6725-1
DOI :
10.1109/ICIP.2001.959147
