基于张量子空间的半脑对称度特征与癫痫识别

结合脑PET图像信息,提出了一种基于张量子空间的半脑对称度特征的识别方法用于识别PET图像中癫痫病灶.首先计算全部脑PET图像中所有体素的SUV,并基于SUV建立三阶张量;然后提取半脑对称度特征,建立半脑对称度张量模型;其次利用多线性主成分分析(MPCA)方法对半脑对称度张量模型进行特征选择;最后基于支持向量机(SVM)分类器进行癫痫识别.实验结果表明:提出的算法能够有效地识别脑PET图像中的癫痫病灶,可以作为计算机辅助诊断方式帮助医生进行癫痫疾病的诊断.

Hemisphere Symmetry Feature Based on Tensor Space and Recognition of Epilepsy

With brain PET(positron emission tomography) image information, a recognition method based on hemisphere symmetry feature of tensor space was proposed to identify the epilepsy lesions of PET(positron emission tomography) images. Firstly, the SUV(standard uptake value) of each voxel in brain PET images was calculated and the third order tensor based on SUV was constructed. Then, the hemisphere symmetry feature was extracted and the hemisphere symmetry tensor model was built. Next, a multi linear principal component analysis (MPCA) algorithm was used for feature selection of hemisphere symmetry tensor model. Lastly, the support vector machine (SVM) was used to identify the epilepsy. The results show that the epilepsy lesions of the brain PET images can be effectively identified by the proposed algorithm, which can be used as a computer aided diagnosis way to help doctors with epilepsy disease diagnosis.