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Magnetic resonance image classification method based on an independent component high-order uncertain brain network

An independent component, image classification technology, applied in the field of image processing, can solve the problems of ignoring the uncertainty of brain network function and unable to fully and accurately reflect the interaction of brain regions.

Active Publication Date: 2018-11-20
TAIYUAN UNIV OF TECH
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Problems solved by technology

Although the deterministic map is relatively simple to analyze, it cannot fully and accurately reflect the interaction between brain regions because it ignores the uncertainty of brain network function.

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  • Magnetic resonance image classification method based on an independent component high-order uncertain brain network
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  • Magnetic resonance image classification method based on an independent component high-order uncertain brain network

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[0076] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0077] An MRI image classification method based on high-order uncertain brain networks with independent components, the process is as follows figure 1 shown, follow the steps below:

[0078] Step S1: Preprocessing the resting-state functional magnetic resonance imaging data, and then extracting independent components by independent component analysis;

[0079] Step S2: Filter out the independent components belonging to the default network, extract the time se...

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Abstract

The invention discloses a magnetic resonance image classification method based on an independent component high-order uncertain brain network and relates to the image processing technology. The methodcomprises the steps of extracting a default network independent component by using independent component analysis, then a high-order functional connection network is constructed on the time series ofthe independent component by using a time window sliding method, a frequent subgraph mining and feature selection method based on a weighted graph is used, and the classification and recognition arecarried out with a discriminant subgraph as a feature. The method does not rely on a priori brain atlas template, time-varying characteristics in a scanning time are fully considered, the frequent subgraph mining is applied to the weighted graph, and more advanced and more complex interaction between brain regions is showed. The problem of the low accuracy of a traditional magnetic resonance imagedata classification method is solved.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a magnetic resonance image classification method based on independent component high-order uncertain brain network. Background technique [0002] The human brain is a highly complex system, and exploring its inner structure and function is a great challenge. The combination of functional magnetic resonance imaging technology and complex network theory has become one of the research hotspots in the field of brain science and has been widely used in various researches. This approach has yielded many surprising results in exploring structural and functional interactions between brain regions. However, due to the limitations of its own principles and characteristics and methodology, the classification accuracy is low, which seriously affects the application value. [0003] In recent years, most studies based on brain networks have defined network nodes according to brain m...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 郭浩郭涛程忱雷波王恁李瑶李欣芸孙超
Owner TAIYUAN UNIV OF TECH
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