Brain Magnetic Resonance Images Segmentation Based on Wavelet Method

This paper focused on the brain magnetic resonance (MR) images, which is one of the key problems in image processing. A novel segmentation method based on watershed transform and wavelets transform is presented for white matter in thin sliced single-channel brain magnetic resonance scans. The original image is smoothed by using anisotropic filter and over-segmented by the watershed algorithm. Finally, the brain MR image is segmented automatically by using the multicontext wavelets-based thresholding (MCWT) method. The result of the experiment indicates that the algorithm can obtain segmentation result fast and accurately.