Catch Fish Optimization Algorithm for Multi-Threshold Segmentation in Esophageal Cancer CT Image Analysis

This paper proposes a multi-threshold image segmentation method based on the Catch Fish Optimization Algorithm (CFOA), specifically aimed at the analysis of esophageal cancer computerized tomography (CT) images. CFOA is a novel metaheuristic algorithm that simulates the fishing behavior of fishermen, possessing strong global search capabilities and flexible local search strategies, effectively balancing exploration and exploitation, and enhancing segmentation accuracy and efficiency. By combining with the Otsu method, CFOA demonstrates excellent performance in determining the optimal threshold, and is compared with advanced optimization algorithms such as MFO, NRBO, and RSA in experiments. The experimental results show that CFOA is competitive in key metric indicators such as Fitness, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), especially showing superiority in handling the complexity of multimodal histograms. This study confirms the effectiveness and robustness of CFOA in the segmentation of esophageal cancer CT images, providing a new optimization tool for the field of image processing.