Human Brain MR Image Segmentation Based on Level Set Method

Traditional level set method is not suitable to non-uniformly distributed human brain segmentation with complex anatomic structures and shapes. Otsu method merge with level set Chan-Vese model, leading to human brain magnetic resonance (MR) image segmentation was presented based on Level Set method. The image information of intraregional distribution was constructed by Heaviside function, and the variance information of interregional distribution was built by maximum between-class variance. In the process of image segmentation, two parts of the information were integrated each other to guide energy function evolution, and the desired results of human brain segmentation was obtained. Experiments based on two datasets that provided human brain image show that the proposed approach has obvious advantages in similarity metrics and success, and lower error rate,can perfectly complete human brain segmentation.