The Human Brain Image Segmentation

Medical image segmentation is a hard-tough problem in medical image processing and analysis. Among it, brain medical image segmentation is the research focus for its important values. The primary task of my thesis is studying the Visible Human brain image segmentation and MRI brain image segmentation techniques in order to extract the tissues and structures. The achievements of my thesis include: 1. A Visible Human brain color image segmentation method based on maximum entropy has been proposed. Combing the maximum entropy method and Mathematical Morphology method that were only used for gray image originally, our method extracts brain tissues and brain bones that won't be done if single method is used. The examples of 576 brain images show its effectiveness. 2.A new MRI kernel fuzzy clustering algorithm has been proposed for MRI image segmentation. When the difference of the date size is large. The small class may be misclassified or may be merged into big class in fuzzy kernel clustering algorithm. A new objective function is introduced and an additional weighting factor is assigned to each class. Based on this improvement the algorithm gets better performance. The results indicate it could reduce 5% misclassification rate of the white matter, gray matter and CSF than original algorithm.3.When Mumford-Shah model is used for multi-object segmentation, the initialization influences the results seriously. A novel image segmentation approach based on hierarchical Mumford-Shah model has been proposed in this paper, which gets the solution by solving the improved image segmentation equations. The example of brain MRI image segmentation indicates its effectiveness.