DocumentCode :
2622764
Title :
An improved fuzzy clustering approach using possibilist c-means algorithm: Application to medical image MRI
Author :
El harchaoui, Nour-eddine ; Bara, Samir ; Kerroum, Mounir Ait ; Hammouch, Ahmed ; Ouaddou, Mohamed ; Aboutajdine, Driss
Author_Institution :
LRIT, Mohamed V-Agdal Univ., Rabat, Morocco
fYear :
2012
fDate :
22-24 Oct. 2012
Firstpage :
117
Lastpage :
122
Abstract :
Currently, the MRI brain image processing is a vast area of research, several methods and approaches have been used to segment these images (thresholding, region, contour, clustering). In this work, we propose a novel segmentation approach, which is based on fuzzy c-means clustering and using possibilist c-means approach. To validate our approach, we have tested successfully on several datasets of real images MRI. Thus, to show the performance of our method, we compared our results with different segmentation algorithms: k-means, fuzzy c-means, and possibilist c-means.
Keywords :
biomedical MRI; brain; fuzzy set theory; image segmentation; medical image processing; pattern clustering; brain image processing; fuzzy clustering; image segmentation; medical image MRI; possibilist c-means algorithm; Biomedical imaging; Clustering algorithms; Computational modeling; Computers; Image segmentation; Magnetic resonance imaging; Phase change materials; Clustering; Fuzzy cmeans; Image MRI; K-means; Possibilist c-means; Segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Technology (CIST), 2012 Colloquium in
Conference_Location :
Fez
Print_ISBN :
978-1-4673-2726-8
Electronic_ISBN :
978-1-4673-2724-4
Type :
conf
DOI :
10.1109/CIST.2012.6388074
Filename :
6388074
Link To Document :
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