Wavelet Shrinkage Prefiltering for Brain Tissue Segmentation

This paper presents a method to segment brain tissue from T1-weighted magnetic resonance (MR) images. A modified BayesShrink method is utilized to filter the image in wavelet transform domain before segmentation, where the shrinkage strength is automatically adjusted with respect to noise level. Then the fuzzy c-means clustering is applied to segment brain tissue into cerebrospinal fluid, gray matter and white matter. Comparison with other methods for brain tissue segmentation that remove noise using wavelet or non-wavelet based methods is made on phantom or real data and the advantage of the proposed method is demonstrated