Brain Medical Image Recognition Via Tensorization Analysis

With the field of computer innovation and development,medical imaging technology is also constantly improve.The image recognition algorithm occupies a very high position in medical research and clinical trials.Because the brain is the most important organ in the human body,therefore the diagnosis of the brain is highly dependent on medical image recognition techniques.However,the medical image of the brain is a three-dimensional space voxel structure.Traditional image feature algorithms and classification algorithms are usually directed to two-dimensional images.If the traditional image feature algorithms and classification algorithms are used for medical images of the brain,they will ignore the characteristics of the image structure and lose information about the image structure.And this approach eventually leads to a low image recognition rate and a high computational complexity.How to save the spatial structure information at the same time to extract the characteristics of brain medical images,this is a problem that needs to be solved in the field of medical image recognition.This thesis presents a tensorization of data based on neighborhood cyclic displacement,also referred to as TD method.Combined with the Tensorization of Principal Component Analysis(TPCA)we introduced,we can use the TD method to extract the feature of various high dimensional spatial structure medical image data,while saving high-dimensional spatial structure information.TD method expand the scope of application of TPCA.At the same time,we introduce the SBD data set for brain medical images and test with our method.The experimental results show that the TD method has good adaptability in extracting the feature of medical images of high-dimensional spatial structure images.