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