Extraction and Evaluation of Corpus Callosum from 2D Brain MRI Slice: A Study with Cuckoo Search Algorithm.
Algorithms
Autism Spectrum Disorder
/ diagnostic imaging
Case-Control Studies
Computational Biology
Corpus Callosum
/ diagnostic imaging
Databases, Factual
Diagnosis, Computer-Assisted
/ methods
Humans
Image Interpretation, Computer-Assisted
/ methods
Magnetic Resonance Imaging
/ methods
Neuroimaging
/ methods
Journal
Computational and mathematical methods in medicine
ISSN: 1748-6718
Titre abrégé: Comput Math Methods Med
Pays: United States
ID NLM: 101277751
Informations de publication
Date de publication:
2021
2021
Historique:
received:
23
02
2021
revised:
18
05
2021
accepted:
08
07
2021
entrez:
12
8
2021
pubmed:
13
8
2021
medline:
25
11
2021
Statut:
epublish
Résumé
The work proposes a computer-based diagnosis method (CBDM) to delineate and assess the corpus callosum (CC) segment from the 2-dimensional (2D) brain magnetic resonance images (MRI). The proposed CBDM consists of two parts: (1) preprocessing and (2) postprocessing sections. The preprocessing tools have a multithreshold technique with the chaotic cuckoo search (CCS) algorithm and a preferred threshold procedure. The postprocessing employs a delineation process for extracting the CC section. The proposed CBDM finally extracts the vital CC parameters, such as total brain area (TBA) and CC area (CCA) to classify the considered 2D MRI slices into the control and autism spectrum disorder (ASD) groups. This attempt considers the benchmark brain MRI database which includes ABIDE and MIDAS for the experimental investigation. The results obtained with ABIDE dataset are further confirmed against the fuzzy
Identifiants
pubmed: 34381523
doi: 10.1155/2021/5524637
pmc: PMC8352692
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5524637Informations de copyright
Copyright © 2021 K. Suresh Manic et al.
Déclaration de conflit d'intérêts
The authors declare that they have no conflict of interest.
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