Brain Magnetic Resonance Images Segmentation And Its Evaluation

Magnetic resonance imaging is an effective and noninvasive approach to observe human brain. Now Magnetic resonance imaging is widely applied in many fields, such as medicine, neuroscience, psychology and cognitive science because of its advantages. This paper focused on the brain magnetic resonance images, which is one of the key problems in medical 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 then the morphologic grad image is computed, which is the input image over-segmented by the watershed algorithm. After the segmentation, the small regions are incorporated into their neighbor regions according to the comparability of the two regions. The experiment shows that the method can lead a perfect result, and has some robustness. Finally, the brain MR image is segmented automatically by using the multicontext wavelets-based thresholding method. In this method, the wavelet multiscale transform of local image gray histogram is done and the gray threshold is gradually found out from large scale coefficients to small scale coefficients. Image segmentation is independently performed in each local image to calculate the degree of membership of a pixel to each tissue class. A strategy is adopted to integrate the intersected outcomes from different local images. The result of the experiment indicates that the algorithm can obtain segmentation result fast and accurately.Some traditional methods of image segmentation are used and compared with the improved segmentation method based on watershed transform and wavelets transform. They are individually based on probabilistic modeling of intensity distributions which is the basic idea of Statistical Parametric Mapping 2 (SPM2) and Level Set filter of Insight Segmentation and Registration Toolkit (ITK). The result of the experiment indicates that our method not only outperforms other traditional segmentation methods in classifying brain MR images but also more suitable to diffusion tensor imaging data. As to the Visualization, We use a mature method named Marching Cube and it provides comparatively satisfied result.Developing the whole project, we use some popular toolkits in the medical imaging field: Insight Segmentation and Registration Toolkit (ITK) and Visualization Toolkit (VTK). Code is highly organized and is extendable.