Enhancing Brain Segmentation in MRI through Integration of Hidden Markov Random Field Model and Whale Optimization Algorithm

The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automa...

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Bibliographic Details
Published in:Computers (Basel) 2024-05, Vol.13 (5), p.124
Main Authors: Daoudi, Abdelaziz, Mahmoudi, Saïd
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automation
Brain
Brain cancer
Brain research
brain tissue segmentation
Cerebrospinal fluid
Classification
Coefficients
Datasets
Deep learning
Disease
Fields (mathematics)
Fuzzy logic
HMRF method
Image processing
Image segmentation
Ischemia
Magnetic resonance imaging
Mathematical optimization
Medical imaging
Methods
Neural networks
Neuroimaging
Optimization
Optimization algorithms
Research methodology
Tumors
WOA
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Summary:The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automatic approach for robust and accurate brain tissue boundary outlining in MR images. This algorithm is proposed for the tissue classification of MR brain images into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). The proposed segmentation process combines two algorithms, the Hidden Markov Random Field (HMRF) model and the Whale Optimization Algorithm (WOA), to enhance the treatment accuracy. In addition, we use the Whale Optimization Algorithm (WOA) to optimize the performance of the segmentation method. The experimental results from a dataset of brain MR images show the superiority of our proposed method, referred to HMRF-WOA, as compared to other reported approaches. The HMRF-WOA is evaluated on multiple MRI contrasts, including both simulated and real MR brain images. The well-known Dice coefficient (DC) and Jaccard coefficient (JC) were used as similarity metrics. The results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice coefficient and Jaccard coefficient above 0.9.
ISSN:2073-431X
2073-431X
DOI:10.3390/computers13050124