A fuzzy evolutionary algorithm for medical image segmentation

Author

Leïla, Amrane ; Abdelouahab, Moussaoui

Author_Institution

Nat. Sch. of Comput. Sci. (ESI), Algiers, Algeria

fYear

2012

fDate

24-26 March 2012

Firstpage

1

Lastpage

3

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology and e-Services (ICITeS), 2012 International Conference on

Conference_Location

Sousse

Print_ISBN

978-1-4673-1167-0

Type

conf

DOI

10.1109/ICITeS.2012.6216659

Filename

6216659