With the rapid popularization of medical image acquisition devices, medical images have been widely applied in clinical diagnosis. It is important to classify these data efficiently and accurately. The imaging results of medical images show that brain CT images own good texture features and texture angular point positions are approximately the same between images. In this paper, under the guidance of the brain medical domain knowledge, a classification algorithm based on the KAP (K nearest neighbor texture angular points) directed graph model is presented. First of all, the T-Harris method is proposed to extract texture angular points. Then, we use texture angular points and combine with characteristics of medical images to propose the KAP directed graph model. In the end, a medical image classification algorithm based on the KAP directed graph model is proposed. Experimental results show that our algorithm has achieved good results in terms of time complexity and accuracy.
['Ping Wu', 'Haiwei Pan', 'Linlin Gao', 'Qilong Han', 'Xiaoqin Xie', 'Xiaoning Feng']
Medical image classification method based on the KAP directed graph model