In the context of complex structures and blurred cell boundaries in microscopic breast cancer histopathological images, traditional threshold-based segmentation faces challenges in accurately separating lesion areas of breast cancer images. To address this issue, this study proposes a multi-threshold segmentation method for breast cancer images based on the improved dandelion optimization algorithm (IDO). This method introduces the IDO to calculate the maximum inter-class variance (Otsu) as the objective function for finding the optimal thresholds. The IDO incorporates a defensive strategy to address the issue of unbounded search in the traditional dandelion optimization algorithm (DO) that extends beyond pixel ranges. Additionally, opposition-based learning (OBL) is introduced to prevent the algorithm from getting trapped in local optima. The experimental results indicate that compared with the Harris Hawks optimization (HHO), gorilla troop optimization (GTO), traditional DO, and marine predators algorithm (MPA), the IDO algorithm achieves the highest fitness value and fastest convergence under the same number of threshold levels. Moreover, it outperforms other comparative algorithms in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) , and feature similarity index (FSIM).