Image Segmentation: Type-2 Fuzzy Possibilistic C-Mean Clustering Approach
Publish place: 08th International Industrial Engineering Conference
Publish Year: 1391
Type: Conference paper
Language: English
View: 1,297
This Paper With 6 Page And PDF Format Ready To Download
- Certificate
- I'm the author of the paper
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
Export:
Document National Code:
IIEC08_215
Index date: 27 November 2012
Image Segmentation: Type-2 Fuzzy Possibilistic C-Mean Clustering Approach abstract
Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or stronglysupervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crispclustering and even Type-1 fuzzy clustering could not handle. Hence, Type-2 fuzzy clustering is the most preferred method. In recent years, neurology and neuroscience have been significantly advanced by imaging tools, which typically involve vast amount of data and many uncertainties.Therefore, Type-2 fuzzy clustering methods could process these images more efficient and could provide betterperformance. The focus of this paper is to segment the brain Magnetic Resonance Imaging (MRI) in to essential clusters based on Type-2 Possibilistic C-Mean (PCM) method. Theresults show that using Type-2 PCM method provides better results
Image Segmentation: Type-2 Fuzzy Possibilistic C-Mean Clustering Approach Keywords:
Image Segmentation: Type-2 Fuzzy Possibilistic C-Mean Clustering Approach authors
M.H Fazel Zarandi
Amirkabir University ofTechnology (Tehran Polytechnic
A Zarinbal
University of Waterloo
M. Zarinbal
Amirkabir University of Technology (Tehran Polytechnic
H. Izadbakhsh
Azad University