A novel method for adaptive enhancement and unsupervised segmentation of MRI brain image
This paper describes a novel global-to-local method for the adaptive enhancement and unsupervised segmentation of brain tissues in 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 image using wavelet thresholding, and segment the image with minimum error thresholding. Both the thresholdings are global-wise. Subsequently, we combine locally adaptive weighted median and weighted average filters with FCM (fuzzy C-means) clustering to achieve a local-wise segmentation. The performance of the proposed method is quantitatively validated by four indices with respect to a MRI brain phantom.