Department of Electronic Engineering, Tsinghua University, Beijing, China
10.47176/TMI.2025???
Abstract
Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic modality extensively utilized in clinical medicine. In the computational analysis of brain MRI scans, image segmentation yields critical spatial knowledge regarding internal anatomical structures. Although this segmentation process is conventionally performed manually by radiologists, its precise execution is hindered by the complex morphology of soft tissues and the presence of system-induced artifacts or noise. This study introduces an automated image segmentation framework leveraging an Improved Bat Optimization Algorithm. Under this approach, the bat heuristic is hybridized with the $K$-means algorithm to globally optimize the selection of initial cluster centroids. The operational efficiency of the proposed method is evaluated and benchmarked against alternative state-of-the-art techniques. Quantitative simulations executed within the MATLAB environment demonstrate that the proposed framework achieves an outstanding segmentation accuracy of 99.5%, consistently outperforming baseline methods.
Renhua,W . (2025). Brain MRI Segmentation Using an Improved Bat Optimization Algorithm. Transactions on Machine Intelligence, 8(2), 91-100. doi: 10.47176/TMI.2025???
MLA
Renhua,W . "Brain MRI Segmentation Using an Improved Bat Optimization Algorithm", Transactions on Machine Intelligence, 8, 2, 2025, 91-100. doi: 10.47176/TMI.2025???
HARVARD
Renhua W. (2025). 'Brain MRI Segmentation Using an Improved Bat Optimization Algorithm', Transactions on Machine Intelligence, 8(2), pp. 91-100. doi: 10.47176/TMI.2025???
CHICAGO
W Renhua, "Brain MRI Segmentation Using an Improved Bat Optimization Algorithm," Transactions on Machine Intelligence, 8 2 (2025): 91-100, doi: 10.47176/TMI.2025???
VANCOUVER
Renhua W. Brain MRI Segmentation Using an Improved Bat Optimization Algorithm. Trans. Mach. Intell.. 2025;8(2):91-100. doi: 10.47176/TMI.2025???
