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Clustering thought-based multi-view dynamic brain network feature dimension reduction method

A feature dimension, brain network technology, applied in the field of image processing, can solve the problems of inability to provide information for neurological disease diagnosis, large network scale, and high computational complexity, so as to reduce network computational complexity, reduce network scale, and improve The effect of diagnostic accuracy

Active Publication Date: 2020-06-23
中科信息产业(山东)有限公司
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Problems solved by technology

[0003] The existing methods for constructing brain networks have the following disadvantages technically: (1) the network scale is large and the computational complexity is high; (2) the perspective of the constructed brain network is single, and it cannot provide information for the diagnosis of neurological diseases from multiple perspectives. information

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  • Clustering thought-based multi-view dynamic brain network feature dimension reduction method
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  • Clustering thought-based multi-view dynamic brain network feature dimension reduction method

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Embodiment Construction

[0058]The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0059] The present invention provides a multi-view dynamic brain network feature dimension reduction method based on the idea of ​​clustering. The data used is the data obtained by scanning the brain with resting state functional magnetic resonance technology, which is called resting state functional magnetic resonance image. Data or RS-fMRI data, stored in the form of matrix. It includes M=170 volume images of R=160 brain functional areas (hereinafter referred to ...

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Abstract

The invention discloses a clustering thought-based multi-view dynamic brain network feature dimension reduction method, which relates to the technical field of image processing, and comprises the following steps of: clustering vertexes and edges of a built dynamic brain network by using a clustering method, and enabling correlation time sequence rules among the vertexes distributed in the same cluster to be similar; processing each cluster by using a central moment method to obtain a central moment correlation time sequence; then, based on a central moment thought of a central moment, constructing a low-order brain network by using the central moments of the correlation time sequences; furthermore, a high-order dynamic network is constructed on the basis of correlation, and a high-order brain network is constructed by adopting the principle of a low-order dynamic network. According to the method, the reduced low-order dynamic brain network and the high-order dynamic brain network are constructed by using a clustering thought, so that the network scale and the calculation complexity are reduced to a great extent; and establishing a plurality of brain networks by utilizing the central moment characteristics, and providing diagnosis information for disease diagnosis from a plurality of perspectives.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a multi-view dynamic brain network feature dimension reduction method based on the idea of ​​clustering. Background technique [0002] The brain functional connectivity network (hereinafter referred to as the brain network) takes the brain functional area as the vertex of the network, the connection between the functional areas as the edge, and the weight of the edge is the correlation strength between the functional areas. Currently, brain networks can be divided into low-order brain networks and high-order brain networks. Brain networks provide an important method for the diagnosis of neurological diseases, especially dynamic brain networks, which not only reflect the interconnection between brain functional areas, but also reflect the dynamic changes of their connections. [0003] The existing methods for constructing brain networks have the following disadvantages t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G16H50/20
CPCG16H50/20G06F18/23G06F18/2411
Inventor 赵峰张祥飞陈红瑜冯烟利谢青松
Owner 中科信息产业(山东)有限公司
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