Medical image classification method based on self-weighted grading biological characteristic, device and computer-readable storage medium

A biometric, medical image technology, applied in medical images, computer parts, computer-aided medical procedures, etc., can solve the problem that pMCI and sMCI cannot be effectively distinguished, cannot fully reflect the bias of MCI patients, and information cannot accurately transmit MCI. Patients and other problems, to achieve the effect of improving classification accuracy, strong discrimination and robustness

Active Publication Date: 2019-08-16
山东管理学院
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Problems solved by technology

Although the information of AD and NC populations can be transferred to MCI patients by using spine regression method, this method does not make full use of the relationship information between brain regions in the regression process, so the information of AD and NC populations cannot be passed through spine regression. Coefficients are accu

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  • Medical image classification method based on self-weighted grading biological characteristic, device and computer-readable storage medium
  • Medical image classification method based on self-weighted grading biological characteristic, device and computer-readable storage medium
  • Medical image classification method based on self-weighted grading biological characteristic, device and computer-readable storage medium

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Embodiment Construction

[0036]The present invention will be further described below in conjunction with accompanying drawing and example.

[0037] Such as figure 1 As shown, the specific steps of the medical image classification method based on self-weighting grading biological features of the present invention for pMCI and sMCI classification are as follows:

[0038] (1) Image preprocessing

[0039] The MRI images of 142 AD patients, 165 normal subjects, 126 pMCI patients and 95 sMCI patients were randomly selected from the ADNI database (such as figure 2 shown), using FreeSurfer software for preprocessing to extract two morphological features of cortical thickness (cortical thickness, CT) and volume (volume, VOL). CT refers to the closest distance between the white and gray matter surfaces in each vertex. Then, the image is smoothed using a Gaussian kernel function. Finally, the mean CT and VOL of each anatomical region were calculated using the Automated Anatomical Labeling (AAL) template. I...

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Abstract

The invention provides a medical image classification method based on a self-weighted grading biological characteristic. According to the method, through adding a graph regularization item in a ridgeregression process, neighborhood relation information between brain regions of an MCI patient is sufficiently utilized, and an obtained regression coefficient more accurately reflect an information transmission condition of AD and NC to the MCI. Then a self-weighted grading method is used for performing fusion on the regression coefficient, thereby ensuring a fact that a sample with a relatively high regression coefficient value in a fusion process acquires a relatively high weight and realizing higher distinguishability and robustness of the calculated self-weighted grading biological characteristic. Finally, a support vector machine classifier is used for performing classification. The medical image classification method remarkably improves classification accuracy of pMCI patients and sMCI patients.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a medical image classification method, device and computer-readable storage medium based on self-weighting and hierarchical biological features. Background technique [0002] Alzheimer's disease (AD) is a common progressive neurodegenerative disease. It is predicted that by 2050, there will be 1 AD patient in every 85 people. As the most common type of Alzheimer's disease, AD is accompanied by memory loss, cognitive decline, and language function degeneration, which seriously affects people's normal life. Mild cognitive impairment (MCI) is generally regarded as a transitional state between normal aging and AD, which is characterized by mild memory impairment but basically intact cognitive function in patients. The survey found that more than one-third of MCI patients will transform into AD within 5 years. According to whether MCI can transform into AD within the follow-u...

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Application Information

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IPC IPC(8): G16H50/70G16H30/20G06K9/62
CPCG16H50/70G16H30/20G06F18/2411
Inventor 李颖
Owner 山东管理学院
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