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Multi-scale information fused brain resting state functional magnetic resonance data classification method and system and computer readable medium

A technology of functional magnetic resonance and data classification, which is applied in the direction of computer parts, calculation, and pattern recognition in signals, etc., can solve the problem of low accuracy rate, achieve the effect of solving low accuracy rate and improving classification accuracy

Pending Publication Date: 2022-06-24
XIDIAN UNIV
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Problems solved by technology

[0006] The purpose of the embodiments of the present invention is to provide a brain resting state fMRI data classification method, system, and computer-readable medium that integrate multi-scale information, so as to solve the problem that the existing resting state fMRI data analysis method is based on a single The problem of low accuracy in classifying brain states with scaled brain state data

Method used

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  • Multi-scale information fused brain resting state functional magnetic resonance data classification method and system and computer readable medium
  • Multi-scale information fused brain resting state functional magnetic resonance data classification method and system and computer readable medium
  • Multi-scale information fused brain resting state functional magnetic resonance data classification method and system and computer readable medium

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Experimental program
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Embodiment 2

[0156] The excluded subjects are used to test the ability of the SVM classifier to reliably classify new cases. This process is repeated for each subject to obtain a relatively unbiased estimate of generalization ability. The performance of the algorithm can be measured by its sensitivity, Specificity, accuracy and AUC (area under receiver operating characteristic curve) are described.

[0157] Sensitivity is the proportion of all outcomes that the model correctly predicts with a true value of 1, and is calculated as:

[0158]

[0159] In contrast, specificity is the proportion of all outcomes where the model correctly predicts a true value of -1 (prediction is stable), and is calculated as:

[0160]

[0161] Among them, TP L represents the total number of experts predicted as experts, TN L Indicates the total number of normal controls predicted to be normal controls, FN L represents the total number of normal controls predicted to be experts, FP L Indicates the tota...

Embodiment 3

[0177] An embodiment of the present invention proposes a resting-state functional magnetic resonance data classification system for fused multi-scale information, including: a memory for storing instructions executable by a processor; and a processor for executing the instructions to achieve the above A method for classifying resting-state functional magnetic resonance data of the brain that fuses multi-scale information as described in Embodiment 1.

[0178] A brain resting state fMRI data classification system incorporating multi-scale information may include an internal communication bus, a processor, a read only memory (ROM), a random access memory (RAM), a communication port, and a hard disk. The internal communication bus can realize data communication between components of a brain resting-state fMRI data classification system that integrates multi-scale information. The processor can make judgments and issue prompts. In some embodiments, a processor may consist of one ...

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Abstract

The invention discloses a brain resting state functional magnetic resonance data classification method and system fused with multi-scale information and a computer readable medium, and the method comprises the steps: obtaining resting state functional magnetic resonance imaging data of two groups of people in different states, and carrying out the feature extraction after noise elimination; the method comprises the following steps: respectively extracting brain features of three scales of voxel ontology activity, voxel cluster local covariance and voxel network global covariance, carrying out region division on a brain, then calculating average values of each brain region of each tested object of two groups of crowds in different states on the voxel ontology activity, the voxel cluster local covariance and the voxel network global covariance, and calculating the average values of each brain region of each tested object of the two groups of crowds in different states; under each feature scale, each tested object of two different state crowds obtains a 1 * N-dimensional feature matrix, the feature matrixes under each scale are fused, feature selection is carried out on the fused feature matrixes, an optimal feature subset obtained through feature selection is used for training an SVM classifier, classification of the tested objects is carried out, and the classification accuracy is effectively improved.

Description

technical field [0001] The invention belongs to the field of magnetic resonance image processing, and relates to a method, system and computer-readable medium for classifying resting-state functional magnetic resonance data of the brain by fusing multi-scale information. Background technique [0002] Magnetic resonance imaging technology is a non-invasive brain observation technology. It reflects brain activity by measuring the magnetic resonance signals caused by changes in components such as cerebral blood flow and cerebral blood oxygen caused by neural activity. It is widely used in clinical observation and diagnosis. and cognitive function research. [0003] Neurons are the basic structural unit of brain function, and a comprehensive representation of the information interaction between spatially adjacent or discrete neurons is a necessary prerequisite for accurately distinguishing brain states. Voxel is the basic information unit in magnetic resonance data, and a singl...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62A61B5/055
CPCA61B5/055G06F2218/04G06F2218/08G06F2218/12G06F18/2411G06F18/253
Inventor 董明皓张晓燕张子元张培铭吴佳陈超石光明梁继民
Owner XIDIAN UNIV
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