Resting state function magnetic resonance image data classification method based on high-order super network

Abstract

The present invention relates to the image processing technology, and concretely provides a resting state function magnetic resonance image data classification method based on a high-order super network. The problem is solved that the traditional magnetic resonance image data classification method is low in classification accuracy. The resting state function magnetic resonance image data classification method based on the high-order super network comprises the following steps: the step S1: performing preprocessing of the resting state function magnetic resonance image; the step S2: performing time window segment of the average time sequence of each brain region; the step S3: calculating the Pearson's correlation coefficients between each two average time sequences of each brain region; the step S4: extracting the values of corresponding elements in the Pearson's correlation matrix; the step S5: employing a sparse linear regression model to construct a high-order super network; the step S6: calculating the local attributes of the high-order super network; the step S7: selecting the classification features and constructing a classifier; and the step S8: performing quantification of the importance degree and the redundancy degree of the selected features. The resting state function magnetic resonance image data classification method based on the high-order super network is suitable for the classification of the magnetic resonance image data.

Description

technical field [0001] The invention relates to image processing technology, in particular to a method for classifying resting-state functional magnetic resonance image data based on a high-order hypernetwork. Background technique [0002] The human brain is an extremely complex information processing system. In the field of neuroscience, an important challenge is to reveal its internal function and structural organization mode. As a combination of multimodal magnetic resonance imaging technology and complex network theory, magnetic resonance imaging data classification methods have become one of the hotspots in the field of brain science. However, due to the limitations of its own principles and characteristics, traditional magnetic resonance image data classification methods generally have methodological limitations, which lead to low classification accuracy, which seriously affects its application value. [0003] In traditional resting-state fMRI analysis, functional con...

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Problems solved by technology

[0004] In order to solve the problem of low classification accuracy of traditional magnetic resonance image data classification methods, the

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