Title :
A fuzzy evolutionary algorithm for medical image segmentation
Author :
Leïla, Amrane ; Abdelouahab, Moussaoui
Author_Institution :
Nat. Sch. of Comput. Sci. (ESI), Algiers, Algeria
Abstract :
An unsupervised fuzzy clustering technique, fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. However, the FCM algorithm always converges to strict local minima, starting from an initial guess of the membership degrees. To overcome this limitation of FCM algorithm, a fuzzy evolutional c-mean (FECM) algorithm is presented in this paper. We combine the classical FCM with an evolutional algorithm and we introduce the sharing operator for taking into account the spatial information.
Keywords :
evolutionary computation; fuzzy set theory; image segmentation; mathematical operators; medical image processing; pattern clustering; FCM algorithm; FECM algorithm; fuzzy c-means clustering algorithm; fuzzy evolutional c-mean algorithm; fuzzy evolutionary algorithm; medical image segmentation; sharing operator; unsupervised fuzzy clustering technique; Biological cells; Biomedical imaging; Clustering algorithms; Encoding; Evolutionary computation; Image segmentation; Partitioning algorithms; Clustering; Evolutionary algorithm; Fuzzy c-means; segmentation;
Conference_Titel :
Information Technology and e-Services (ICITeS), 2012 International Conference on
Conference_Location :
Sousse
Print_ISBN :
978-1-4673-1167-0
DOI :
10.1109/ICITeS.2012.6216659
