Brain function magnetic resonance image classification method based on network centrality

A magnetic resonance image, network-centric technology, applied in the field of image processing, can solve problems such as unreasonable assumptions and degradation of classification performance

Active Publication Date: 2013-01-02
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

However, such an assumption is unreasonable in many cases
Functional magnetic resonance images of people in different states will be disturbed by many factors. Traditional classificati

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  • Brain function magnetic resonance image classification method based on network centrality
  • Brain function magnetic resonance image classification method based on network centrality
  • Brain function magnetic resonance image classification method based on network centrality

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[0045] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0046] The brain functional magnetic resonance image classification method based on network centrality provided by the present invention is a brand new brain functional magnetic resonance image classification method. This method first establishes a brain functional network model, and calculates the network centrality of each node in the brain network to represent different image patterns; then uses the adaptive boost (adaboost) classifier to adopt leave-one-out cross validation -validation) to classify images.

[0047] refer to figure 1 , figure 1 It is a flow chart of a method for classifying brain functional magnetic resonance images based on network centrality according to an embodiment of the present invention, and ...

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Abstract

The invention discloses a brain function magnetic resonance image classification method based on network centrality. The method comprises the following steps of: preprocessing a brain function magnetic resonance image, performing brain region segmentation, and extracting an average time sequence of each brain region; calculating a partial correlation coefficient between each average time sequence, and obtaining a partial correlation coefficient matrix; performing binarization on the partial correlation coefficient matrix to obtain a brain network model; calculating the network centrality of each node in the network; and classifying the brain function magnetic resonance image by utilizing an adaptive improvement classifier, and checking the adaptive improvement classifier by employing a leave-one-out cross validation testing method. The brain function network is established by utilizing the brain function magnetic resonance image, the brain function magnetic resonance image is classified by utilizing the network topology information, and the brain function magnetic resonance image can be accurately classified.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for classifying brain functional magnetic resonance images based on network centrality. Background technique [0002] Functional Magnetic Resonance Imaging (fMRI) has been widely used in the diagnosis and treatment of neurological diseases due to its high spatial and temporal resolution and non-invasive features. fMRI generally refers to magnetic resonance imaging based on blood oxygen level-dependent (BOLD), which reflects changes in the brain by measuring changes in magnetic resonance signals caused by changes in cerebral blood flow and cerebral blood oxygen caused by neural activity. Activity. The brain is a complex system, and magnetic resonance images of the brain respond to stimulating conditions or lesions. It is an important application of computer-aided analysis to use image classification methods to calculate the possibility of certain at...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 田捷刘振宇白丽君
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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