Alzheimer's disease cortex auto-classification method based on multi-scale mesh surface form features

Abstract

The invention discloses an Alzheimer's disease cortex auto-classification method based on multi-scale mesh surface form features. The method includes the steps of determining two sample groups, namely an AD (Alzheimer's disease) group and an NC (normal control) group, and dividing the sample groups into a sample set and a test set under equal proportion; extracting multi-scale mesh surfaces from brain MRI (magnetic resonance imaging) images of samples; calculating LVPD (local vertex point distance) and average curvature for each vertex; with the smoothed LVPD and average curvature, extracting areas having a significant statistical difference and screening two seed points in index sense; extracting a feature row vector for each sample of the training set to form a feature matrix, and training a classifier with the reduced dimensionality feature matrix and corresponding sample classes; testing performance of the classifier with the samples in the test set. By the use of the Alzheimer's disease cortex auto-classification method based on multi-scale mesh surface form features, the defects the prior art is susceptible to cortex segmentation errors and certain-scale difference may be missed are overcome, and the two sample groups can be classified according to the cortex multi-scale form features.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to an automatic classification method for Alzheimer's disease cerebral cortex based on multi-scale grid surface shape features, which can detect Alzheimer's disease cases by using the shape features of cerebral cortex , has the role of clinical auxiliary diagnosis. Background technique [0002] As the problem of population aging becomes increasingly prominent, pay attention to the quality of life of the elderly, pay attention to diseases of the elderly including Alzheimer's Disease (AD), and explore new methods and means for understanding, preventing and treating such diseases It has practical value in improving the overall quality of life of the society. [0003] The causes of AD are complex, the development process is gradual, and the clinical manifestations vary. Especially in the early stage, there are no obvious clinical symptoms. Before the imagin...

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Problems solved by technology

[0007] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art, to propose a method for automatic classification of Alzheimer's disease cerebral cortex based on multi-scale grid surface shape features, to overcome the susceptibility to cortical segmentation errors of commonly used AD cerebral cortex classification methods Effects and possible disadvantages of missing differences at a certain scale, enabling multi-scale classification of Alzheimer's disease samples

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