Intuitive Fuzzy C-Means Algorithm for MRI Segmentation

Abstract

A new model called intuitive fuzzy c-means (IFCM) model is proposed for the segmentation of magnetic resonance image in this paper. Fuzzy c-means (FCM) is one of the most widely used clustering algorithms and assigns memberships to which are inversely related to the relative distance to the point prototypes that are cluster centers in the FCM model. In order to overcome the problem of outliers in data, several models including possibilistic c-means (PCM) and possibilistic-fuzzy cmeans (PFCM) models have been proposed. In IFCM, a new measurement called intuition level is introduced so that the intuition level helps to alleviate the effect of noise. Several numerical examples are first used for experiments to compare the clustering performance of IFCM with those of FCM, PCM, and PFCM. A practical magnetic resonance image data set is then used for image segmentation experiment. Results show that IFCM compares favorably to several clustering algorithms including the SOM, FCM, CNN, PCM, and PFCM models. Since IFCM produces cluster prototypes less sensitive to outliers and to the selection of involved parameters than the other algorithms, IFCM is a good candidate for data clustering and image segmentation problems.