3D unsupervised modified spatial fuzzy c-means method for segmentation of 3D brain MR image

Abstract

This paper proposed a novel 3D unsupervised spatial fuzzy-based brain MRI volume segmentation technique in the presence of intensity inhomogeneity and noise. Instead of static masking, dynamic 3D masking has been proposed to measure the correlation among neighbors. The local membership function is defined based on the weighted correlation among neighbors. The local and global membership functions are combined to suppress the inhomogeneity and noise at the time of clustering. A weighted function is defined based on the 3D dynamic neighborhood to optimize the objective function in 3D space. In 2D slice-based MRI image segmentation techniques, the selection of the slice of interest is very important and it depends on the experience and skills of the expertise. As the proposed unsupervised method segments the 3D brain MRI volume as a whole, there is no need of such expertise. The detailed analysis of the results shows that the proposed unsupervised spatial fuzzy-based 3D brain MRI image segmentation technique is superior over the considered state-of-the-art methods in terms of cluster validation functions.