Research on different automatic segmentation methods for color cascading framework in detecting malaria infection

Malaria remains a global health challenge due to its significant mortality and morbidity worldwide. Our study presents a comparative analysis of five automated segmentation models such as Fuzzy C-Means (FCM), K-Means, Gaussian Mixture Model (GMM), Entropy Filtering, and Watershed, for detecting malaria infection in microscopic blood smear images. The study uses dataset of 574 images, consisting of four Plasmodium species. The study uses dataset of 574 images, comprising four Plasmodium species (P. falciparum, P. malariae, P. ovale, and P. vivax) across three developmental stages: trophozoites, schizonts, and gametocytes. Preprocessing techniques used is namely Color Cascading Method, including RGB normalization, gamma correction, noise reduction, exposure compensation, and edge enhancement, were employed to optimize image quality prior to segmentation. Experimental results demonstrate that FCM consistently outperforms other methods, achieving an average accuracy of 98.26%, specificity of 97.91%, and sensitivity of 98.61%. The robustness of FCM in detecting faint and overlapping structures highlights its suitability for automated malaria diagnosis systems, particularly in resource-limited settings. While Entropy Filtering shows promise as an alternative, its inconsistent performance across different lifecycle stages limits its practical utility. The findings of this study provide a foundation for developing accurate and accessible automated diagnostic tools to enhance malaria detection and support global eradication efforts.