Image Segmentation Based on Interval Type 2 Fuzzy Clustering and Differential Immune Clone Algorithm

The segmentation of brain magnetic resonance (MR) images plays an important role in the computer aided diagnosis and clinical research. However, due to presence of noise and uncertainty on the boundary between different tissues in the brain image, the segmentation of brain image is a challenging task. Many variants of standard fuzzy c-means (FCM) algorithm have been proposed to handle the noise. In this paper, a new method based on interval type 2 fuzzy clustering and differential immune clone algorithm for image segmentation is proposed. By replacing hard clustering with fuzzy clustering through incorporating interval type 2 fuzzy clustering into differential immune clone algorithm, this algorithm can obtain more abundant clustering information. Specially, as the advantage of interval type 2 fuzzy set is processing uncertain data, the proposed algorithm is more conducive to solve the uncertainty problem. In experiments, the obtained segmentation results on MR brain image demonstrate the efficacy of the proposed algorithm and superior performance in comparison to existing segmentation methods.