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VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation

VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation

Mariem Miledi, Souhail Dhouib
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 17
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799861126|DOI: 10.4018/IJAMC.2021010106
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MLA

Miledi, Mariem, and Souhail Dhouib. "VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation." IJAMC vol.12, no.1 2021: pp.94-110. http://doi.org/10.4018/IJAMC.2021010106

APA

Miledi, M. & Dhouib, S. (2021). VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation. International Journal of Applied Metaheuristic Computing (IJAMC), 12(1), 94-110. http://doi.org/10.4018/IJAMC.2021010106

Chicago

Miledi, Mariem, and Souhail Dhouib. "VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation," International Journal of Applied Metaheuristic Computing (IJAMC) 12, no.1: 94-110. http://doi.org/10.4018/IJAMC.2021010106

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Abstract

Image segmentation is a very crucial step in medical image analysis which is the first and the most important task in many clinical interventions. The authors propose in this paper to apply the variable neighborhood search (VNS) metaheuristic on the problem of brain magnetic resonance images (MRI) segmentation. In fact, by reviewing the literature, they notice that when the number of classes increases the computational time of the exhaustive methods grows exponentially with the number of required classes. That's why they exploit the VNS algorithm to optimize two maximizing thresholding functions which are the between-class variance (the Otsu's function) and the entropy thresholding (the Kapur's function). Thus, two versions of the VNS metaheuristic are respectively obtained: the VNS-Otsu and the VNS-Kapur. These two novel proposed thresholding methods are tested on a set of benchmark brain MRI to show their robustness and proficiency.

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