Medical image segmentation based on simulated annealing and opposition-based learning island algorithm - JiMing, M. A.; HongYu, Duan; YuFan, Wang; LiNa, Wang (PloS one, 2024)
With the development of society and changes in the human living environment, people are increasingly attaching importance to their own health. Regarding medical imaging examinations of certain parts of the body, the process of medical image segmentation has become extremely important. This paper presents a novel hybrid algorithm: SAOBL-IA, a fusion of the Simulated Annealing(SA), Opposition-based Learning(OBL)and Island Algorithm(IA). The Island Algorithm itself suffers from slow convergence speed and the tendency to get stuck in local optimum. To address these limitations, we introduce opposition-based learning to enhance the search range and avoid local optimum. Furthermore, we leverage the simulated annealing approach to accelerate the convergence of SAOBL-IA. Comparing the experimental results, it can be seen that SAOBL-IA has better comprehensive performance. Subsequently, the SAOBL-IA algorithm is utilized in conjunction with an optimized two-dimensional OTSU fusion segmentation technique for the purpose of medical image processing. This study proposes an application of image segmentation based on the SAOBL-IA. The segmentation of pixels around the background and target regions using the two-dimensional OTSU method faces challenges in terms of accuracy. To address this issue, an adaptive thresholding technique known as Adaptive Forking is employed for optimization. By determining the slope of the fork based on the misclassified pixel ratio, enhanced segmentation accuracy can be achieved. This improved approach is then integrated with the SAOBL-IA algorithm and applied to the segmentation of lung medical images. The experimental findings show that the amalgamation of SAOBL-IA with the adaptive two-dimensional OTSU segmentation approach, as proposed in this study, manifests superior segmentation speed and enhanced precision in the context of medical image segmentation.