An Enhanced Watershed Transformation Approach for MRI Gray Matter Segmentation Using Iterative Parallel Region Merging

Abstract

Accurate segmentation of cortical gray matter is important for a study of central nervous system diseases. Slice-by-slice manual segmentation of the cortical gray matter is a tedious and time consuming process. Automatic or semiautomatic segmentation using computer make the tough job easier for the radiologist to analyze the cortical gray matter. Among the existing segmentation algorithms, watershed transformation has proved to be very useful and powerful tool for morphological image segmentation because of its moderate computational complexity and its ability to identify vital closed boundaries of a given image even if the image contrast is poor. However, it exhibits over segmentation when applied to segment magnetic resonance image cortical gray matter. This work attempts to overcome the problem of oversegmentation by make use of a pre-segmentation and postsegmentation processes. Fuzzy filtering is employed as the pre-segmentation process which reduces the additional formation of local minima due to noise in the segmentation stage. A post-segmentation process, iterative parallel region merging is proposed in this paper which unites over segmented regions and identifies the existing natural segments of the magnetic resonance image.