Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2013, Article ID 435497, 12 pages http://dx.doi.org/10.1155/2013/435497 Research Article Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI Nour-Eddine El Harchaoui, 1 Mounir Ait Kerroum, 1,2 Ahmed Hammouch, 1,3 Mohamed Ouadou, 1 and Driss Aboutajdine 1 1 LRIT-CNRST URAC 29, Mohammed V-Agdal University, Faculty of Science, BP 1014, Rabat, Morocco 2 LARIT Equipe Imagerie et Multimedia, Ibn Tofail University, Faculty of Science, ENCG, BP 242, K´ enitra, Morocco 3 LRGE, Mohammed V-Souissi University, ENSET, Rabat Instituts, BP 6207, Rabat, Morocco Correspondence should be addressed to Nour-Eddine El Harchaoui; noureddine.elharchaoui@gmail.com Received 29 May 2013; Revised 29 September 2013; Accepted 31 October 2013 Academic Editor: Daoqiang Zhang Copyright © 2013 Nour-Eddine El Harchaoui et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te 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 classifcation 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 classifcation algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. Te experiments were realized on diferent synthetics data sets and real brain MR images. 1. Introduction Image segmentation is a very important operation in the process of treatment and analyzing images, and it is widely used in the diferent felds: pattern recognition, remote sensing, artifcial intelligence, medical imaging, and so forth. Te feld of medical imaging includes several types of images: radiography (X-ray), ultrasound and magnetic resonance image [1–4]. Tese images are a very complex data, so their analysis is a challenge for researches. In the literature, there are several methods that can segment these images. We can group them in four classes. Te frst one is the Tresholding ; it allows to fnd the optimal threshold value, in order to extract the background objects in the image. In general, this approach is very sensitive to noise and ignores the spatial parameters [5, 6]. Te second approach is the Contour; it allows to detect the contour of the image. Tis method is easy to implement, but unfortunately it is very sensitive to the noise and also to the parameters initialization, which means that it is mostly used with a pretreatment flter [7–10]. Te third approach is the Region, which generates some methods: growing region (called ascendant) and split- ting/merging (called descendants); this approach is very sensitive to the initial parameters and to the noise [11–13]. Te last approach is the Clustering ; it is a very important operation in the process and data analysis, and it allows cre- ating the homogeneous partitions using a similarity criterion [3, 4, 14–33]. In this work, we are interested in clustering segmentation using the possibility theory combined with fuzzy theory. Te remainder of this paper is structured as follows. In Section 2, we present the clustering approach with three con- ventional algorithms, fuzzy c-means (FCM) (Algorithm 1), possibilistic c-means (PCM) (Algorithm 2), and possibilis- tic fuzzy c-means (PFCM) (Algorithm 3). In Section 3,