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

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. (C) 2003 Elsevier B.V. All rights reserved.