An Alzheimer's disease MRI image classification method based on an SVM-RFE-MRMR algorithm

A classification method and algorithm technology, which is applied in the field of medical image classification, can solve the problems of unstable recognition accuracy, high labor intensity, and low efficiency, so as to avoid image reading classification errors, reduce labor intensity, and ensure accuracy and recognition rate Effect

Active Publication Date: 2019-06-04
贵州联科卫信科技有限公司
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Problems solved by technology

But because the task performed by radiologists or clinical experts is a tedious and time-consuming task, and the final classification accuracy depends only on their experience, the current classification recognition of MRI images of AD, MCI and NC is laborious. High intensity, low efficiency and unstable recognition accuracy

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  • An Alzheimer's disease MRI image classification method based on an SVM-RFE-MRMR algorithm
  • An Alzheimer's disease MRI image classification method based on an SVM-RFE-MRMR algorithm
  • An Alzheimer's disease MRI image classification method based on an SVM-RFE-MRMR algorithm

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Embodiment 1

[0109] Example 1. A kind of Alzheimer's disease MRI image classification method based on SVM-RFE-MRMR algorithm, carry out according to the following steps:

[0110] a. Use the VBM (voxel-based morphology analysis) method to determine the lesion area in the MRI image, calculate the gray matter volume of the lesion area as the morphological feature, and extract the texture features including the gray-level co-occurrence matrix and the gray-gradient co-occurrence matrix; The invention combines the three-dimensional information (that is, gray matter volume) and two-dimensional information (that is, the gray level co-occurrence matrix and the gray level-gradient co-occurrence matrix) of the MRI image to extract the morphological features and texture features. Through the combination of the two, the accuracy of the MRI image is improved. Recognition rate.

[0111] b. Combining the morphological features and texture features described in step a (for example: there are three kinds o...

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Abstract

The invention discloses an Alzheimer's disease MRI image classification method based on an SVM-RFE-MRMR algorithm. The method comprises the following steps of: a, determining a focus area in an MRI image by adopting a VBM method, calculating the gray matter volume of the focus area as morphological characteristics, and extracting texture features including a gray level co-occurrence matrix and agray level-gradient co-occurrence matrix; b, combining the morphological features and the textural features in the step a, and using the SVM-RFE-MRMR algorithm to extract the features of the combination to obtain the features of the combination after selection; and c, using the SVM-RFE algorithm to carry out feature sorting on the selected combination features, classifying the features by adoptingan SVM algorithm of a radial kernel function after sorting, and normalizing the data of the selected combination features to be between [0, 1] before classification. The identification method provided by the invention has the characteristics of low labor intensity, high efficiency, high accuracy and high identification rate.

Description

technical field [0001] The invention relates to the field of medical image classification, in particular to an Alzheimer's disease MRI image classification method based on the SVM-RFE-MRMR algorithm. Background technique [0002] Medical image processing is the most challenging and emerging field today. Extracting, identifying and segmenting lesion regions from magnetic resonance (MRI) brain images is an important problem. For example, the classification and recognition of MRI images of patients with Alzheimer's disease (AD), patients with mild cognitive impairment (MCI) and normal individuals (NC). But because the task performed by radiologists or clinical experts is a tedious and time-consuming task, and the final classification accuracy depends only on their experience, the current classification recognition of MRI images of AD, MCI and NC is laborious. The situation of high intensity, low efficiency and unstable recognition accuracy. Contents of the invention [000...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06T7/00
Inventor 李晖施若冯刚
Owner 贵州联科卫信科技有限公司
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