A Novel Approach for Segmentation of MRI Brain Images

Abstract

A novel method for segmentation of brain tissues in MRI (magnetic resonance imaging) images is proposed in this paper. First, we reduce noise using a versatile wavelet-based filter. Subsequently, watershed algorithm is applied to brain tissues as an initial segmenting method. Normally, the result of classical watershed algorithm on grey-scale textured images such as tissue images is over-segmentation. The following procedure is a merging process for the over-segmentation regions using fuzzy clustering algorithm (fuzzy C-means). But there are still some regions which are not divided completely, particularly in the transitional regions of gray matter and white matter, or cerebrospinal fluid and gray matter. This motivated the construction of a re-segmentation processing approach to partition these regions. We exploited a method base on minimum covariance determinant (MCD) estimator to detect the regions needed segmentation again, and then partition them by a supervised k-nearest neighbor (kNN) classifier. This integrated approach yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments.