Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/16954
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dc.contributor.authorMaliki, Danlami-
dc.contributor.authorMuazu, M.B-
dc.contributor.authorKolo, J.G-
dc.contributor.authorOlaniyi, O.M-
dc.date.accessioned2023-01-10T08:49:38Z-
dc.date.available2023-01-10T08:49:38Z-
dc.date.issued2022-
dc.identifier.citationMaliki, 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.comen_US
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/16954-
dc.description.abstractMedical 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.en_US
dc.language.isoenen_US
dc.publisherATBU Journal of Science Technology and Education (JOSTE)en_US
dc.subjectElite opposition bacterial foraging optimizationen_US
dc.subjectimage registrationen_US
dc.subjecttransformation parametersen_US
dc.subjectroot mean square erroren_US
dc.titleUnimodal Medical Image Registration Using Elite Opposition Bacterial Foraging Optimization Algorithmen_US
dc.typeArticleen_US
Appears in Collections:Computer Engineering

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