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.

International Conference on Acoustics Speech and Signal Processing (ICASSP)