基于对称性理论的医学图像多阶段分类算法

A large number of medical CT images collected by PACS are widely used in clinical diagnosis. ROI and the features of ROI extracted from CT images can be utilized to classify these medical images so as to assist doctors to improve the efficiency and precision of the diagnosis. Brain imaging shows that it is approximately symmetrical about the brain stem. Based on this medical knowledge guidance, a medical image multi-stage classification (MSC) based on the theory of symmetry is presented in this paper. First of all, weak symmetry and strong symmetry is defined to describe the symmetry from the different granularities. Then, the weak symmetry decision algorithm was given to finish the first-stage classification for medical image in the coarse granularity. Further, the strong symmetry decision algorithm based on the point symmetry is proposed, combining with the weak symmetry decision algorithm, to complete the second-stage classification for the abnormal images classified from the first-stage in the fine granularity in order to locate lesion area. Finally, the features extracted from the lesions are used for the third-stage classification to help the doctor's diagnosis. Experimental results show that multi-stage classification method based on the theory of symmetry can increase the accuracy of the classification and reduce the time of the doctor's diagnosis.