Progressive type mild cognitive impairment identification method based on neuroimaging

A technology of cognitive dysfunction and identification method, which is applied in the field of computer information image processing, can solve problems such as insufficient samples, achieve high accuracy, high classification performance, and solve the effects of insufficient samples

Inactive Publication Date: 2016-11-09
ANHUI UNIVERSITY OF TECHNOLOGY
View PDF2 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] In order to overcome the deficiencies of the prior art, the present invention proposes a progressive mild cognitive impairment identification method based on neuroimaging. The present invention uses the random projection dimensionality reduction method to perform dimensionality reduction processing on the data, which r...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Progressive type mild cognitive impairment identification method based on neuroimaging
  • Progressive type mild cognitive impairment identification method based on neuroimaging
  • Progressive type mild cognitive impairment identification method based on neuroimaging

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0040] see figure 1 , a method for identifying progressive mild cognitive impairment based on neuroimaging in this embodiment is carried out according to the following specific steps:

[0041] Step 1. Image preprocessing:

[0042] Obtain basic information such as gender, age, MMSE (Mini Mental State Examination) score and CDR (Clinical Dementia Rating) score of the test sample from the ADNI database (Alzheimer's disease neuroimaging research database), and select the subjects in the study. The method of controlling variables is applied to make the MCI-C and MCI-NC samples as equal in age as possible, that is, the ages are both between 60-90 years old, and the approximate distribution is the same, for example, the MCI between 60-70 years old The number of samples for both C and MCI-NC is 15 to eliminate the influence of age factors. The MMSE score is above 24 and the CDR is 0.5. These two scores are one of the criteria for judging whether the test sample is MCI First, downloa...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a progressive type mild cognitive impairment identification method based on neuroimaging, and belongs to the technical field of computer image processing. The MRI (Magnetic Resonance Imaging) graph and the PET (Positron Emission Tomography) graph of a test sample are downloaded from an ADNI (Alzheimer's Disease Neuroimaging Initiative) database and are subjected to preprocessing and sample screening to obtain N groups of sample images; the AAL (Anatomical Automatic Labeling) template of the human is selected to independently manufacture 90 cerebral region templates for the sample images, and the grey matter voxel value of a corresponding cerebral region is obtained to obtain N*180-dimensional data; and finally, a second level integration classifier is constructed, feature dimension reduction is carried out on the obtained data, a reduced dimension is subjected to optimization, and the data is applied to the second level integration classifier to carry out classification identification on progressive type MCI (Mild Cognitive Impairment) patients and non-progressive type MCI patients. The data is subjected to the dimension reduction processing by a random projection method, then, the data is applied to the second level integration classifier, classification accuracy is 74.22%, sensitivity is 66.25%, specificity is 82.19%, operation speed is improved, and the classification accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of computer information image processing, and more specifically, to a neuroimage-based identification method for progressive mild cognitive impairment. Background technique [0002] Alzheimer's disease (Alzheimer's Disease, AD) is a fatal neurodegenerative disease, which seriously damages the health of the elderly. The incidence rate of the elderly over 80 years old in my country is 30%. Impairment (MCI) is considered to be a clinical state between normal aging and AD. Not all MCI patients will progress to AD, so mild cognitive impairment (MCI) is the best period for prevention and treatment of Alzheimer's disease (AD). However, if we want to give effective preventive interventions during the MCI period, we must be able to effectively identify and distinguish patients with progressive MCI (MCI-C) and patients with non-progressive MCI (MCI-NC). [0003] With the development of imaging medicine and computer i...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62G06F19/00
CPCG06F18/2411G06F18/214
Inventor 王兵徐燕会汪文艳刘晓东聂建华王培珍周芳陶锋
Owner ANHUI UNIVERSITY OF TECHNOLOGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products