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Classifying method for functional magnetic resonance image data based on multi-scale brain network characteristics

A technology of functional magnetic resonance and image data, applied in the field of image processing, can solve problems such as low classification accuracy, achieve the effects of high application value, improve classification accuracy, and solve low classification accuracy

Inactive Publication Date: 2017-01-04
TAIYUAN UNIV OF TECH
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Problems solved by technology

[0003] In order to solve the problem of low classification accuracy of traditional magnetic resonance image data classification methods, the present invention provides a functional magnetic resonance image data classification method based on multi-scale brain network features

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  • Classifying method for functional magnetic resonance image data based on multi-scale brain network characteristics
  • Classifying method for functional magnetic resonance image data based on multi-scale brain network characteristics
  • Classifying method for functional magnetic resonance image data based on multi-scale brain network characteristics

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Embodiment Construction

[0016] A functional magnetic resonance imaging data classification method based on multi-scale brain network features, which is implemented by the following steps:

[0017] Step S1: Preprocessing the resting-state fMRI images;

[0018] Step S2: According to the selected standardized brain atlas, use the dynamic random seed method to segment the preprocessed resting-state fMRI images. The segmentation scales are 90, 256, 497, 1003, 1501, and then segment Each brain area extracts the average time series;

[0019] Step S3: Using the Pearson correlation method, calculate the degree of correlation between the average time series of each brain region, and thus obtain the correlation matrix;

[0020] Step S4: setting a threshold, and then binarizing the correlation matrix according to the threshold, thereby obtaining a resting state functional brain network model;

[0021] Step S5: Calculating the local attributes of the resting-state functional brain network model and the AUC valu...

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Abstract

The invention relates to an image processing technique and specifically relates to a classifying method for functional magnetic resonance image data based on multi-scale brain network characteristics. The invention solves the problem of low classifying accuracy of the traditional magnetic resonance image data classifying method. The classifying method for functional magnetic resonance image data based on multi-scale brain network characteristics comprises the following steps: S1) pre-processing the resting state functional magnetic resonance image; S2) adopting a dynamic random seed method for performing region segmentation on the image and extracting mean time sequences for the cut brain areas; S3) calculating a relevance degree of each two mean time sequences of the brain areas; S4) performing binarization processing on the incidence matrix; S5) calculating a local property of the resting state functional brain network and an AUC value thereof in a specific threshold space; S6) constructing a classifier; S7) quantizing the importance and redundancy of the selected characteristics in the classifier. The classifying method provided by the invention is fit for the classification of the magnetic resonance image data.

Description

technical field [0001] The invention relates to image processing technology, in particular to a functional magnetic resonance imaging data classification method based on multi-scale brain network features. Background technique [0002] As a combination of functional Magnetic Resonance Imaging (fMRI) technology and complex network theory, MRI data classification methods have become one of the hotspots in the field of brain science. However, due to the limitations of its own principles, traditional MRI data classification methods can only describe a single-scale brain network, resulting in low classification accuracy, which seriously affects its application value. Based on this, it is necessary to invent a new classification method for magnetic resonance image data to solve the above-mentioned problems existing in traditional magnetic resonance image data classification methods. Contents of the invention [0003] In order to solve the problem of low classification accuracy ...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 郭浩曹锐王彬相洁李海芳陈俊杰
Owner TAIYUAN UNIV OF TECH
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