The complex structure and blurry cell boundaries in breast cancer histopathological tissue images under a microscope pose challenges for accurate segmentation using traditional thresholding methods. To address the problem of accurately separating lesion areas in breast cancer images, this study proposes a multi-threshold segmentation method based on the Enhanced Dandelion Optimization algorithm (IDO).The proposed method incorporates the concept of opposite-based learning and utilizes IDO to compute the maximum between-class variance (OTSU) as the objective function. It also establishes fallback strategies and a memory matrix while combining the golden jackal energy judgment mechanism to search for the optimal thresholds. Experimental comparisons are conducted by applying IDO algorithm along with Harris Hawks Optimization (HHO), Gorilla Troops Optimization (GTO), traditional Dandelion Optimization (DO), and Marine Predators Algorithm (MPA) using the same thresholds for image segmentation. The performance analysis is based on four metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Mean Squared Error (MSE), as well as their fitness function curves.The experimental results demonstrate that the Enhanced Dandelion Optimization algorithm outperforms other algorithms in terms of PSNR, SSIM, FSIM, and MSE segmentation metrics. It exhibits faster convergence and effectively resolves the segmentation problem.