An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images

Abstract

This paper presents an integrated method of the adaptive enhancement for an unsupervised global-to-local segmentation of brain tissues in three-dimensional (3-D) MRI (Magnetic Resonance Imaging) images. Three brain tissues are of interest: CSF (CerebroSpinal Fluid), GM (Gray Matter), WM (White Matter). Firstly, we de-noise the images using a newly proposed versatile wavelet-based filter, and segment the images with minimum error global thresholding . Subsequently, we combine a spatial-feature-based FCM (Fuzzy C-Means) clustering with 3-D clustering-result-weighted median and average filters, so as to further achieve a locally adaptive enhancement and segmentation. This integrated strategy yields a robust and accurate segmentation, particularly in noisy images . The performance of the proposed method is validated by four indices on MRI brain phantom images and on real MRI images.