Fuzzy Clustering Algorithm-Segmented MRI Images in Analysis of Effects of Mental Imagery on Neurorehabilitation of Stroke Patients

The study focused on the automatic segmentation of Magnetic Resonance Imaging (MRI) images of stroke patients and the therapeutic effects of Mental Imagery on motor and neurological functions after stroke. First, the traditional fuzzy c-means (FCM) algorithm was optimized, and the optimized one was...

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Bibliographic Details
Published in:Scientific programming Vol. 2021; pp. 1 - 10
Main Authors: Su, Haoliang, Wang, Fang, Zhang, Leying, Li, Guiyang
Format: Journal Article
Language:English
Published: New York Hindawi 28.07.2021
John Wiley & Sons, Inc
Subjects:
Algorithms
Brain
Brain research
Clustering
Disease
Fuzzy sets
Image segmentation
Magnetic resonance imaging
Medical imaging
Patients
Rehabilitation
Stroke
Therapists
Training
Vital signs
ISSN:1058-9244, 1875-919X
Online Access:Get full text
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Summary:The study focused on the automatic segmentation of Magnetic Resonance Imaging (MRI) images of stroke patients and the therapeutic effects of Mental Imagery on motor and neurological functions after stroke. First, the traditional fuzzy c-means (FCM) algorithm was optimized, and the optimized one was defined as filter-based FCM (FBFCM). 62 stroke patients were selected as the research subjects and randomly divided into the experimental group and the control group. The control group accepted the conventional rehabilitation training, and the experimental group accepted Mental Imagery on the basis of the control group. They all had the MRI examination, and their brain MRI images were segmented by the FBFCM algorithm. The MRI images before and after treatment were analyzed to evaluate the therapeutic effects of Mental Imagery on patients with motor and nerve dysfunction after stroke. The results showed that the segmentation coefficient of the FBFCM algorithm was 0.9315 and the segmentation entropy was 0.1098, which were significantly different from those of the traditional fuzzy c-means (FCM) algorithm. (P<0.05), suggesting that the FBFCM algorithm had good segmentation effects on brain MRI images of stroke patients. After Mental Imagery, it was found that the patient’s Function Independent Measure (FIM) score was 99.04 ± 8.19, the Modified Barthel Index (MBI) score was 51.29 ± 4.35, the Fugl-Meyer (FMA) score was 61.01 ± 4.16, the neurological deficit degree in stroke (NFDS) score was 11.48 ± 2.01, the NIH Stroke Scale (NIHSS) score was 10.36 ± 1.69, and the clinical effective rate was 87.1%, all significantly different from those of the conventional rehabilitation training group (P<0.05). Additionally, the brain area activated by Mental Imagery was more extensive. In conclusion, the FBFCM algorithm demonstrates superb capabilities in segmenting MRI images of stroke patients and is worth promotion in clinic. Mental Imagery can promote the neurological rehabilitation of patients by activating relevant brain areas of patients.
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ISSN:1058-9244
1875-919X
DOI:10.1155/2021/9945153