基于张量子空间的半脑对称度特征与癫痫识别.

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. [ABSTRACT FROM AUTHOR]