基于支持向量机和条件随机场的MR图像分割

研究针对颅内各组织的MRI图像的新型分割算法.利用支持向量机(support vector machine, SVM)在解决高维及非线性问题的优势和条件随机场(conditional random field, CRF)有效学习数据之间局部依赖关系的优势,将SVM与CRF相结合,提出了多分类的支持向量机条件随机场分割算法(SVM-CRF),并应用于MR图像中各脑组织的分割.实验结果显示,对于较易识别的脑脊液,SVM-CRF算法比SVM算法和CRF算法的分割精度分别提高了1.83%和5.81%;对于较难识别的骨松质,SVM-CRF算法比SVM算法和CRF算法的分割精度分别提高了1.84%和7.60%.理论分析与实验结果表明,SVM-CRF算法的分割精度均明显优于SVM和CRF算法,并且对于较难识别的组织,该算法的优势更能得以体现.

Segmentation of MR Image Based on Support Vector Machine and Conditional Random Field

A novel segmentation algorithm which was called SVM-CRF for encephalic tissues in MR images was proposed. It is known that support vector machine (SVM) is a powerful machine learning algorithm for solving high dimension and non-linearity problems, and conditional random field (CRF) is effective in learning the dependency among the local data. The SVM and the CRF were combined to obtain the SVM-CRF segmentation algorithm, aiming to take their own advantages to improve the image segmentation accuracy. Experimental results show that the segmentation accuracy of the SVM-CRF is better than that of the SVM and the CRF, which is increased by 1.83% and 5.81% respectively for the cerebrospinal fluid, and increased by 1.84% and 7.60% respectively for the cancellous substance. Theoretical analysis and experimental results indicate that the SVM-CRF has better accuracy than the SVM and the CRF respectively for tissue image segmentation, especially for the tissue image which is difficult to be identified.