Multi-threshold image segmentation for melanoma based on Kapur’s entropy using enhanced ant colony optimization - Yang, Xiao; Ye, Xiaojia; Zhao, Dong; Heidari, Ali Asghar; Xu, Zhangze; Chen, Huiling; Li, Yangyang (Frontiers in Neuroinformatics, 2022)
Melanoma is a malignant tumor formed by the cancerous transformation of melanocytes, and its medical images contain a lot of information. However, the percentage of the key information in the image is small and the noise is non-uniformly distributed. We propose a new multi-threshold image segmentation model based on the two-dimensional (2D) histogram approach to the above problem. In the proposed model, we present an enhanced ant colony optimization for continuous domains (EACOR) based on the soft besiege strategy and the chase strategy. Further, EACOR is combined with 2D Kapur's entropy to search for the optimal thresholds. An experiment on the IEEE CEC2014 benchmark function was conducted to measure the reliable global search capability of the EACOR algorithm in the proposed model. Moreover, we have also carried out several sets of experiments to test the validity of the image segmentation model proposed in this paper. The experimental results show that the segmented images obtained from the proposed model outperform the comparison method in several evaluation metrics. Ultimately, the model proposed in this paper can provide high-quality samples for subsequent analysis of melanoma pathology images.