Brain functional magnetic resonance image classification method based on complex network

A magnetic resonance image and complex network technology, applied in the field of image processing, can solve problems such as unreasonable assumptions and degradation of classification performance, and achieve the effect of improving accuracy and stability and accurate classification

Active Publication Date: 2012-06-20
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 functional magnetic resonance image classification method based on complex network
  • Brain functional magnetic resonance image classification method based on complex network
  • Brain functional magnetic resonance image classification method based on complex network

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Embodiment Construction

[0019] 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.

[0020] Functional magnetic resonance image classification based on complex networks is a new classification method for functional magnetic resonance images of brain. The method first establishes a complex brain network model, and calculates the characteristic path length and clustering degree of the brain network to represent different image patterns; then uses the characteristic path length and clustering degree to train an adaptive boost (adaboost) classifier; finally The test sample image is classified by using the trained adaptive boosting (adaboost) classifier.

[0021] refer to figure 1 According to a method for classifying human brain fMRI images according to the present invention, the category of the test sample ...

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Abstract

The invention relates to a brain functional magnetic resonance image classification method based on a complex network, which comprises the following steps: pre-processing training sample images and test sample images, carrying out region segmentation, and extracting an average time sequence from each region; calculating the partial correlation coefficient among the average time sequences, carrying out matrix binarization on the partial correlation coefficient to obtain a complex network model, and calculating the feature path length, cost and clustering degree of the complex network model to respectively obtain network features of the training sample images and the test sample images; training to obtain an adaboost classifier; and by using the adaboost classifier obtained by training, classifying the test sample images. By using information in the brain functional magnetic resonance images as much as possible, the method can accurately classify the brain functional magnetic resonance images.

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 a complex network. 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 characteristics. fMRI generally refers to magnetic resonance imaging based on blood oxygen level-dependent (BOLD), which reflects the changes of magnetic resonance signals caused by changes in cerebral blood flow and cerebral blood oxygen caused by neural activities to reflect the 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 attributes...

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Application Information

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IPC IPC(8): G06K9/66
Inventor 田捷白丽君刘振宇
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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