Brain function magnetic resonance image classification method based on network centrality

Abstract

The invention discloses a brain function magnetic resonance image classification method based on network centrality. The method comprises the following steps of: preprocessing a brain function magnetic resonance image, performing brain region segmentation, and extracting an average time sequence of each brain region; calculating a partial correlation coefficient between each average time sequence, and obtaining a partial correlation coefficient matrix; performing binarization on the partial correlation coefficient matrix to obtain a brain network model; calculating the network centrality of each node in the network; and classifying the brain function magnetic resonance image by utilizing an adaptive improvement classifier, and checking the adaptive improvement classifier by employing a leave-one-out cross validation testing method. The brain function network is established by utilizing the brain function magnetic resonance image, the brain function magnetic resonance image is classified by utilizing the network topology information, and the brain function magnetic resonance image can be accurately classified.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for classifying brain functional magnetic resonance images based on network centrality. Background technique [0002] Functional Magnetic Resonance Imaging (fMRI) has been widely used in the diagnosis and treatment of neurological diseases due to its high spatial and temporal resolution and non-invasive features. fMRI generally refers to magnetic resonance imaging based on blood oxygen level-dependent (BOLD), which reflects changes in the brain by measuring changes in magnetic resonance signals caused by changes in cerebral blood flow and cerebral blood oxygen caused by neural activity. Activity. The brain is a complex system, and magnetic resonance images of the brain respond to stimulating conditions or lesions. It is an important application of computer-aided analysis to use image classification methods to calculate the possibility of certain at...

AI Technical Summary

Problems solved by technology

However, such an assumption is unreasonable in many cases

Functional magnetic resonance images of people in different states will be disturbed by many factors. Traditional classification methods do not classify functional magnetic resonance images of brain according to the inherent properties of the brain, which will lead to a decline in classification performance.

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