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. 1. Introduction Image segmentation is a very important operation in the process of treatment and analyzing images, and it is widely used in the different fields: pattern recognition, remote sensing, artificial intelligence, medical imaging, and so forth. The field of medical imaging includes several types of images: radiography (X-ray), ultrasound and magnetic resonance image [1–4]. These 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. The first one is the Thresholding; it allows to find 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]. The second approach is the Contour; it allows to detect the contour of the image. This 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 filter [7–10]. The third approach is the Region, which generates some methods: growing region (called ascendant) and splitting/merging (called descendants); this approach is very sensitive to the initial parameters and to the noise [11–13]. The last approach is the Clustering; it is a very important operation in the process and data analysis, and it allows creating the homogeneous partitions using a similarity criterion [3, 4, 14–33]. In this work, we are interested in clustering segmentation using the possibility theory
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