Muda, TZTTZTMudaSalam, RARASalam2024-05-292024-05-2920131951-6851WOS:000335510100115https://oarep.usim.edu.my/handle/123456789/11387Image segmentation is an important phase in image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present a comparative analysis on several segmentation algorithms. Three selected common approaches, that are Fuzzy c-means, K-means and Mean-shift were presented. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method that is K-means was selected. K-means has been enhanced by integrating Median-cut algorithm to further improve the segmentation process. The proposed integrated method has shown a significant improvement in the number of selected regions.en-USSegmentationBlood Cell ImagesMeans-shiftFuzzy c-meansK-meansMedian-cutProceedings Paper47447768