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Multi-modal magnetic resonance image data classification method based on minimal spanning tree

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

Active Publication Date: 2017-03-29
山西三川孪生数字科技有限公司
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Problems solved by technology

[0007] In order to solve the problem of low classification accuracy of traditional magnetic resonance image data classification methods, the present invention provides a multi-modal magnetic resonance image data classification method based on minimum spanning tree

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  • Multi-modal magnetic resonance image data classification method based on minimal spanning tree
  • Multi-modal magnetic resonance image data classification method based on minimal spanning tree
  • Multi-modal magnetic resonance image data classification method based on minimal spanning tree

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Embodiment Construction

[0021] A method for classifying multimodal magnetic resonance imaging data based on a minimum spanning tree, which is implemented by the following steps:

[0022] Step S1: Preprocess the resting-state fMRI images, and perform regional segmentation on the pre-processed resting-state fMRI images according to the selected standardized brain atlas, and then average time for each segmented brain region sequence extraction;

[0023] Step S2: Calculate the covariance coefficient between pairs of average time series of each brain region, thereby obtaining the covariance matrix; then calculate the Pearson correlation coefficient between pairs of average time series of each brain region according to the covariance matrix, From this, the Pearson correlation matrix is ​​obtained;

[0024] Step S3: Preprocessing the MRI DTI images, and segmenting the preprocessed MRI DTI images according to the selected standardized brain atlas, and then performing whole-brain segmentation on each segment...

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Abstract

The invention relates to a multi-modal magnetic resonance image data classification method based on a minimal spanning tree, belongs to image processing technologies, and solves the problem that a traditional magnetic resonance image data classification method is low in classification accuracy. The method is realized by the following steps that S1) a resting-modal function magnetic resonance image is pre-processed; S2) a Pearson's correlation coefficient between average time sequences of every two encephalic regions is calculated; S3) a magnetic resonance dispersion tensor imaging image is preprocessed; S4) the weights of every two encephalic regions are calculated; S5) a multi-modal minimal spanning tree brain network is constructed; S6) local attributes of the multi-modal minimal spanning tree brain network are calculated; S7) a classifier is constructed; and S8) the significance and redundancy of a selected characteristic in the classifier are quantified. The method is suitable for classification of magnetic resonance image data.

Description

technical field [0001] The invention relates to image processing technology, in particular to a minimum spanning tree-based multimodal magnetic resonance image data classification method. Background technique [0002] 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. It is widely used in all kinds of research, especially the research of brain diseases, and has achieved many surprising results. [0003] Functional magnetic resonance imaging (fMRI) has a good application prospect in the study of diseases characterized by functional changes due to its advantages of non-invasiveness, high resolution and repeatability. Functional magnetic resonance is to continuously capture the imaging of the same part in a time series within a short period of time, and the time resolution is high. However, due to the inherent weak...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06V2201/03G06F18/24323
Inventor 郭浩郭涛曹锐相洁李海芳陈俊杰
Owner 山西三川孪生数字科技有限公司
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