Automatic Brain Tissue Extraction Approach of Magnetic Resonance Head Images

Abstract

提出了一种针对MRI磁共振图像通过两次分割实现颅脑图像脑组织自动获取的方法.通过基于Catt扩散模型的各项异性滤波,实现了在保持图像细节的同时有效地消除图像的噪声.然后通过改进的基于相似性区域合并的分水岭算法解决了过分割问题,实现了脑组织区域的初次分割.由于颅脑图像不同组织之间边缘模糊且自身容易受到噪声的影响,导致区域合并过程中可能会误将非脑组织作为脑组织合并,因此,采用水平集方法将初次分割获得的脑组织轮廓作为初始轮廓曲线,实现了脑组织的自动分割.实验结果验证了算法的可行性和实用性.;This paper presents an effective automatic method of brain extraction through twice segmentations respectively. The noise of the image is eliminated through anisotropic diffusion filtering based on Catt model while the details of the image are kept. The over-segmentation problem of the watershed algorithm is solved based on the merging of gray-scale similarity regions and the brain tissue is initially segmented. The edges of different organizations of every brain image are fuzzy and the MRI data is vulnerable to be affected by noise, therefore the non-brain region may be mistaken for brain region in the process of merging. In order to solve these problems, the Level Set method is adopted. The outline of the watershed is taken as the initial curves of level set to realize the automatic segmentation of brain tissue. The feasibility and practicality of this algorithm is proved by the results in the experiments.