MOLMVO-Rényi: An Effective Segmentation Scheme for Prostate Cancer Pathology Images

Recently, the development of artificial intelligence technologies has accelerated advancements in threshold segmentation techniques based on intelligent optimization algorithms, which have demonstrated good performance across various types of medical images. However, research specifically focused on the segmentation of Prostate Cancer Pathology Image (PCPI) remains limited. To fill this gap and improve the efficiency of prostate cancer diagnosis, this study proposes a threshold segmentation scheme based on intelligent optimization algorithms, MOLMVO-Rényi, specifically designed for PCPI. This scheme first enhances the optimization capabilities of the multi-verse optimizer algorithm using multiple strategies (multi-population topological structure and orthogonal learning) to propose MOLMVO. Then it utilizes MOLMVO to optimize the thresholds for image segmentation, aiming to maximize the Rényi entropy of the segmented images. Experimental validation on 67 privately collected PCPIs indicates that the proposed scheme shows significant competitive advantages, providing strong support for the early diagnosis and treatment of prostate cancer.