Unsupervised approach data analysis based on fuzzy possibilistic clustering: application to medical image MRI

Authors:
Nour-Eddine El Harchaoui;Mounir Ait Kerroum;Ahmed Hammouch;Mohamed Ouadou;Driss Aboutajdine
Affiliations:
LRIT-CNRST URAC 29, Mohammed V-Agdal University, Rabat, Morocco;LRIT-CNRST URAC 29, Mohammed V-Agdal University, Rabat and LARIT Equipe Imagerie et Multimedia, Ibn Tofail University, Kénitra, Morocco;LRIT-CNRST URAC 29 and LRGE, Mohammed V-Agdal University, Rabat;LRIT-CNRST URAC 29, Mohammed V-Agdal University, Rabat, Morocco;LRIT-CNRST URAC 29, Mohammed V-Agdal University, Rabat, Morocco
Venue:
Computational Intelligence and Neuroscience
Year:
2013

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Abstract

The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM) to initialize the parameters of possibilistic c-means (PCM), in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.