Muda T.Z.T.Salam R.A.2024-05-282024-05-282011978146000000010.1109/ICCSCE.2011.61905292-s2.0-84862059145https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862059145&doi=10.1109%2fICCSCE.2011.6190529&partnerID=40&md5=21ca4f91df11a5970948f797d9e2ac62https://oarep.usim.edu.my/handle/123456789/8777In blood cell image analysis, segmentation is crucial step in quantitative cytophotometry. Blood cell images have become particularly useful in medical diagnostics tools for cases involving blood. In this paper, we present a better approach on merging segmentation algorithms of K-means and Median-cut for colour blood cells images. Median-cut technique will be employed after comparing best outcomes from Fuzzy c-means, K-means and Means-shift. We used blood cell images infected with malaria parasites as cell images for our research. The result of proposed method shows better improvement in terms of object segmentations for further feature extraction process. � 2011 IEEE.en-USBlood Cell ImagesFuzzy c-meansK-meansMeans-shiftMedian-cutSegmentationBlood cell imagesFuzzy C meanK-meansMeans-shiftMedian-cutBloodCellsControl systemsCytologyFeature extractionFuzzy systemsImage segmentationConference Paper2372436190529