Fractional order calculus enhanced dung beetle optimizer for function global optimization and multilevel threshold medical image segmentation.

With the development of computer vision and medical image processing technology, lung CT scan image segmentation plays an increasingly important role in clinical diagnosis. Doctors can receive precise anatomical structure information from the accurate segmentation of complicated lung structures from CT scan images, which can help with disease diagnosis and treatment planning. However, the complexity and diversity of lung structures make it difficult to design and optimize segmentation algorithms, especially in the presence of lesions, masses, or scars. To solve this problem, the study proposes an enhanced dung beetle optimizer with the fractional order calculus strategy (FDBO) to identify the lung CT scan image’s optimal thresholds. In the FDBO, the fractional order calculus strategy is used to store past individual information and apply it to future individuals to obtain higher-quality individuals. Additionally, adaptive technology is introduced to balance the algorithm’s ability between exploration and exploitation, and the global best position is mutated to enhance the ability of the algorithm to escape from the local optimum. To evaluate the performance of the FDBO, on the one hand, the FDBO is used to solve the CEC2019 benchmark functions in the function global optimization task. On the other hand, the FDBO is applied to the multilevel threshold image segmentation problem with the Otsu method, and the inter-class variance is used as the objective function to solve for its maximum. The experiment’s numerical results show that the FDBO has excellent optimization accuracy and stability. When the threshold is high, the FDBO can obtain better segmentation quality than the other seven algorithms for most lung CT scan images. [ABSTRACT FROM AUTHOR]