Early and late mild cognitive impairment classification method and device based on brain function network characteristics

Abstract

The invention discloses an early and late mild cognitive impairment classification method and device based on brain function network characteristics and belongs to the medical image processing technology field. According to the method, firstly, sample data is pre-processed, multiple brain region time series are extracted, the brain function network is constructed by utilizing a correlation coefficient between the Pearson's correlation calculation brain area time series, and brain network parameters are calculated; secondly, the stepwise analysis method is utilized to extract characteristics, abinary classifier is trained, corresponding characteristic vectors are extracted from to-be-classified resting state functional magnetic resonance data and are inputted to the trained binary classifier, and the medical image classification result is acquired. Compared with a method in the prior art, the method is advantaged in that classification accuracy, sensitivity and specificity are better.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to the classification of early and late mild cognitive impairment diseases by using resting state functional magnetic resonance imaging (fMRI) technology and brain function network characteristics. Background technique [0002] Mild cognitive impairment (MCI) is an intermediate stage between healthy aging and dementia. Studies have shown that the probability of MCI transferring to Alzheimer's disease (AD) is about 10% to 15% every year, while the conversion rate of normal elderly people to AD is in the range of 1% to 2%. As an intermediate stage in the transition from normal aging to dementia, MCI has received extensive attention. According to the degree of memory impairment in MCI disease, MCI patients can be divided into early MCI patients (EMCI) and late MCI patients (LMCI). However, there are differences in multidimensional information between EMCI...

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Problems solved by technology

Studies have shown that in MCI patients, there are significant differences between slow-4 and slow-5 in the posterior cingulate, medial prefrontal cortex, and parahippocampal gyrus

It can be seen that frequency band classification is a new research direction, but few studies use different frequency band functional networks to classify and predict EMCI and LMCI, so it is necessary to propose a medical imaging image classification that uses different frequency band functional networks to classify and predict EMCI and LMCI Approach

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