Open Access

ARTICLE

Fusion Strategy for Improving Medical Image Segmentation

by Fahad Alraddady¹, E. A. Zanaty², Aida H. Abu bakr³, Walaa M. Abd-Elhafiez^4,5,*

1 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
2 Information System Department, Faculty of Computers and Information, Sohag University, Sohag, Egypt
3 Mathematical and Computer Science Department, Faculty of Science, Aswan University, Aswan, Egypt
4 College of Computer Science & Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia
5 Computer Science Department, Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, Egypt

* Corresponding Authors: Walaa M. Abd-Elhafiez. Email: ,

Computers, Materials & Continua 2023, 74(2), 3627-3646. https://doi.org/10.32604/cmc.2023.027606

Received 22 January 2022; Accepted 21 April 2022; Issue published 31 October 2022

Abstract

In this paper, we combine decision fusion methods with four meta-heuristic algorithms (Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, modification of Cuckoo Search (CS McCulloch) algorithm and Genetic algorithm) in order to improve the image segmentation. The proposed technique based on fusing the data from Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms are obtained for improving magnetic resonance images (MRIs) segmentation. Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods. In order to obtain parts of the points that determine similar membership values, we apply the different rules of incorporation for these groups. The proposed approach is applied to challenging applications: MRI images, gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical images. It is shown that the proposed method gives accurate results; due to the decision fusion produces the greatest improvement in classification accuracy.

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Keywords

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