一种基于马尔可夫随机场的弥散张量成像(DTI)图像分割的新算法

A Novel DTI Image Segmentation Algorithm Based on Markov Random Field

目的:提出一种基于马尔可夫随机场(MRF)的弥散张量成像(DTI)图像分割的算法。方法:利用马尔可夫随机场模型,挖掘图像中的弥散张量信息,根据贝叶斯定理将图像分割问题转化为最小后验能量的求取,运用迭代条件模型求解。结果:该算法对DTI图像分割效果明显优于K均值算法,且该效果亦优于该算法对常规MRI T_2WI图像的分割效果。结论:该算法能够充分利用弥散张量矩阵蕴含的空间上下文信息实现DTI图像的有效分割。

Objective To propose a novel Markov random field（MRF） based segmentation algorithm for diffusion tensor images（DTI）.Methods The distance measure defined by Frobenius norm was introduced in order to utilize more spacial information of the diffusion tensor matrix of image voxels.The segmentation issue was transformed to the Minimum A Posteriori（MAP） by Beyes theorem,and the Iterative Conditional Model（ICM） algorithm was employed to achieve the solution of latter MAP problem.Results The comparison of segmentation results between the proposed algorithm and Kmeans segmentation algorithm for DT-MRI image was made,which indicated that the proposed algorithm could segment the DTI images more accurately than the K-means algorithm.Moreover,with the same segmentation algorithm of MRF,better outcomes were achieved in DTI image than that in conventional MRI T2WI image.Conclusion The proposed algorithm can adequately utilize spacial information contained in voxel＇s diffusion tensor matrix to achieve the efficient segmentation of DTI images.