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Alzheimer's disease feature extraction method and system based on collective correlation coefficients

A correlation coefficient and feature extraction technology, applied in biometric identification, computational models, biological models, etc., can solve Alzheimer's disease feature extraction research, the application of genetic algorithm without overall correlation coefficient, and the optimization efficiency needs to be further Improvement and other issues to achieve the effect of improving the efficiency of feature extraction and improving the efficiency of feature optimization

Active Publication Date: 2018-07-06
GUANGDONG POLYTECHNIC NORMAL UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, there is no report on the combination of overall correlation coefficient and genetic algorithm for feature extraction, and there is no application of genetic algorithm based on overall correlation coefficient in the research of Alzheimer's disease feature extraction. Key features of Alzheimer's disease, optimization efficiency needs to be further improved

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  • Alzheimer's disease feature extraction method and system based on collective correlation coefficients
  • Alzheimer's disease feature extraction method and system based on collective correlation coefficients
  • Alzheimer's disease feature extraction method and system based on collective correlation coefficients

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Embodiment 1

[0095] Aiming at the current bottleneck problem of extracting key features for classification and recognition of Alzheimer's disease, mild cognitive impairment and normal health based on magnetic resonance imaging to effectively improve the classification effect but the optimization efficiency is not high, this implementation In this example, a genetic algorithm based on the overall correlation coefficient is proposed to optimize the feature extraction process, so as to find the key features that affect the conversion of different stages of Alzheimer's disease in a shorter time, so as to provide assistance for the research of computer-aided diagnosis of Alzheimer's disease .

[0096] Taking the Alzheimer's disease classifier as a Gaussian process classifier, the input is 100 MRI images, and the output is the key features reflecting the essence of Alzheimer's disease as an example, the specific implementation process of the genetic algorithm based on the overall correlation coef...

Embodiment 2

[0140] In order to illustrate the effect of the feature extraction method of the present invention, corresponding experiments are specially designed for verification in this embodiment. The experimental software of this experiment uses MATLAB2017a and FreeSurfer v5.3.0, and the experimental image is in the three-dimensional format.NII.

[0141] The specific implementation process of the experiment of this embodiment includes:

[0142] (1) Data acquisition

[0143] The data used in this embodiment comes from ADNI (Alzheimer's Disease Neuroimaging Initiative), a large public database of Alzheimer's disease in the United States. The selection criteria for the experimental data is to select data with a balanced ratio of male to female, and the TR / TE values ​​of the imaging parameters must be the same. In this way, the interference of some unknown factors can be eliminated, and the inter-individual differences are small. Therefore, a 3.0T MR scanner was selected in this embodiment...

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Abstract

The invention discloses an Alzheimer's disease feature extraction method and an Alzheimer's disease feature extraction system based on collective correlation coefficients. The Alzheimer's disease feature extraction method comprises the steps of: acquiring magnetic resonance imaging data of the Alzheimer's disease; and adopting a genetic algorithm based on the collective correlation coefficients toperform feature optimization on the acquired magnetic resonance imaging data, so as to obtain key features of the Alzheimer's disease, wherein the genetic algorithm based on the collective correlation coefficients regards the collective correlation coefficients as heuristic knowledge and regards an optimal classification effect as a target to extract the key features. The Alzheimer's disease feature extraction method and the Alzheimer's disease feature extraction system adopt the genetic algorithm based on the collective correlation coefficients to perform feature optimization on the acquiredmagnetic resonance imaging data, combine the collective correlation coefficients with the genetic algorithm to optimize the traditional feature extraction process, improve the feature optimization efficiency of the genetic algorithm by regarding the collective correlation coefficients as heuristic knowledge, regard the optimal classification effect as the target, effectively improve the feature extraction efficiency on the premise of ensuring the classification effect, and can be widely applied to the field of data mining.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a method and system for extracting features of Alzheimer's disease based on overall correlation coefficient. Background technique [0002] Alzheimer's disease (Alzheimer's Disease, AD) is an irreversible chronic neurodegenerative disease and a persistent high-level neurological dysfunction. The existing drug treatments for AD are very limited, but early and accurate detection and treatment can slow down the disease process. Mild cognitive impairment (Mild Cognitive Impairment, MCI) is a transitional stage between normal healthy people (Health Controllers, HC) and AD, and MCI patients are a high-risk population for AD. Studies at home and abroad have pointed out that the important pathological signs and biomarkers of AD can be measured by Magnetic Resonance Imaging (MRI). The method of extracting effective features from MRI to classify and identify the three stages of AD, MCI and HC i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/00
CPCG06N3/006G06V40/10G06V2201/03G06F18/214
Inventor 潘丹曾安
Owner GUANGDONG POLYTECHNIC NORMAL UNIV
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