Abstract: Partitional clustering techniques such as K-Means (KM), Fuzzy C-Means (FCM), and Rough K-Means (RKM) are very simple and efective techniques for image segmentation. But, because their initial cluster centers are randomly determined, it is often seen that certain clusters converge to local optima. In addition to that, pathology image segmentation is also problematic due to uneven lighting, stain, and camera settings during the microscopic image capturing process. Therefore, this study proposes an Improved Slime Mould Algorithm (ISMA) based on opposition based learning and diferential evolution’s mutation strategy to perform illumination-free White Blood Cell (WBC) segmentation. The ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some extent. This paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to fnd the efect of illumination over color pathology image clustering. Numerical and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image segmentation. ISMA-KM and “ab” color channels of CIELab color space provide best results with above-99% accuracy for only nucleus segmentation. Whereas, for entire WBC segmentation, ISMA-KM and the “CbCr” color component of YCbCr color space provide the best results with an accuracy of above 99%. Furthermore, ISMA-KM and ISMA-RKM have the lowest and highest execution times, respectively. On the other hand, ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efcient Nature-Inspired Optimization Algorithms (NIOAs).

Key words: Pathology image , · Image segmentation , · Clustering , · Color space , · White blood cell , · Optimization , · Swarm intelligence , · Fuzzy clustering , · Rough clustering