DocumentCode
1695477
Title
Image segmentation using probabilistic fuzzy c-means clustering
Author
Pham, Tuan D.
Author_Institution
Analog Design Autom. Inc, Ottawa, Ont., Canada
Volume
1
fYear
2001
fDate
6/23/1905 12:00:00 AM
Firstpage
722
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing, 2001. Proceedings. 2001 International Conference on
Conference_Location
Thessaloniki
Print_ISBN
0-7803-6725-1
Type
conf
DOI
10.1109/ICIP.2001.959147
Filename
959147
Link To Document