Auxiliary Recognition of Alzheimer’s Disease Based on Gaussian Probability Brain Image Segmentation Model

Abstract

Alzheimer’s disease is an important disease that threatens the health of the elderly after cardiovascular disease, cerebrovascular disease and cancer. Early diagnosis and early intervention have an inestimable effect on disease control and treatment. Especially for China, which is facing the problem of population aging, early detection and early treatment are particularly important. According to the neuroimaging study of disease, by studying the degree of local brain loss in patients with Alzheimer’s disease, the disease information of the disease manifested in the brain structure is revealed, such as the decrease of the volume of the hippocampus and the thickness of the medial frontal temporal cortex. Thin and so on. In this paper, the local Gaussian probability image segmentation model is used to segment and extract the brain nuclear magnetic image, and the image segmentation of the hippocampus structure is extracted. The local Gaussian probability algorithm of image segmentation extraction algorithm is designed and optimized. The maximal posterior probability principle and Bayes’ rule are introduced to optimize the algorithm by grayscale processing of local image. Therefore, the Gaussian probability model is used to obtain the local mean and standard deviation as a function of spatial variation. Therefore, the probability model is more suitable for image segmentation with uneven gray scale than the probability model based on global hypothesis. Finally, experiments are carried out to verify the correctness of the theory and the robustness of Gaussian probability brain image segmentation.