Unimodal Medical Image Registration using Elite Opposition Bacterial Foraging Optimization Algorithm
| dc.contributor.author | Maliki, D., Muazu, M.B., Kolo J.G., & Olaniyi, O.M | |
| dc.date.accessioned | 2025-05-04T17:43:01Z | |
| dc.date.issued | 2022-09-08 | |
| dc.description | This paper addresses the ongoing challenge in medical image registration of accurately identifying optimal transformation parameters that align a reference image with a floating image. Traditional approaches, including metaheuristic optimization techniques, have been widely applied to this problem. The study critiques the standard Bacterial Foraging Optimization Algorithm (BFOA) for its limitations in exploration ability and convergence speed. In response, the paper proposes an improved algorithm—Elite Opposition Bacterial Foraging Optimization Algorithm (EOBFOA)—which integrates an Elite Opposition Strategy to enhance the search efficiency and accuracy of the original BFOA. The algorithm's effectiveness is validated using Root Mean Square Error (RMSE) as the evaluation metric. Comparative performance analysis shows that EOBFOA significantly outperforms other nature-inspired algorithms in finding the most accurate transformation parameters, thereby improving the quality of unimodal medical image registration. | |
| dc.description.abstract | Medical imaging applications frequently use image registration for a variety of purposes, and the search of an ideal image transformation parameters that align the two images (reference and floating) is still an optimization challenge. Medical image registration has been optimized using different metaheuristics optimization strategies. One method, the Bacterial Foraging Algorithm (BFOA), has issues of poor exploration and low convergence to a better solution. This research work presents the Elite Opposition Bacterial Foraging Optimization Algorithm (EOBFOA) for optimizing unimodal medical image registration. The EOBFOA is an enhanced version of Bacterial Foraging Algorithm (BFOA) using the Elite Opposition Strategy. The proposed EOBFOA uses Root Mean Square Error (RMSE) as a measure to determine the accuracy of the image registration process. The performance of the image registration using the EOBFOA was compared against other existing nature inspired algorithms. The obtained results shown that the proposed EOBFOA outperformed other algorithms in searching for the best optimum transformation parameters for the image registration. | |
| dc.identifier.citation | Maliki, D., Muazu, M.B., Kolo J.G., & Olaniyi, O.M. (2022). Unimodal Medical Image registration Using Elite Opposition Bacterial Foraging Optimization Algorithm. ATBU Journal of Science Technology and Education (JOSTE), 10(3), pp. 263–271, available at: www.atbuftejoste.com. | |
| dc.identifier.uri | http://repository.futminna.edu.ng:4000/handle/123456789/1817 | |
| dc.language.iso | en | |
| dc.publisher | JOURNAL OF SCIENCE TECHNOLOGY AND EDUCATION | |
| dc.subject | Elite opposition bacterial foraging optimization | |
| dc.subject | image registration | |
| dc.subject | transformation parameters | |
| dc.subject | root mean square error | |
| dc.title | Unimodal Medical Image Registration using Elite Opposition Bacterial Foraging Optimization Algorithm | |
| dc.type | Article |