Jabar F.H.A.Ismail W.Salam R.A.Hassan R.2024-05-282024-05-282013978148000000010.1109/ACSAT.2013.802-s2.0-84904205399https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904205399&doi=10.1109%2fACSAT.2013.80&partnerID=40&md5=a87c1dc9ddfd06552747e52822f5bba1https://oarep.usim.edu.my/handle/123456789/8603Clustering is one of the most common automated image segmentation techniques used in many fields including machine learning, pattern recognition, image processing, and bioinformatics. Recently many scientists have performed tremendous research in helping the hematologists in the issue of segmenting the blood cells in the early of prognosis. This paper aims to segment the blood cell images of patients suffering from acute leukemia using an adaptive K-Means clustering together with mean shift algorithm. The integrated clustering techniques have produced comprehensive output images with minimal filtering process to remove the background scene. � 2013 IEEE.en-USacute leukemia cellsclusteringimage segmentationk-meansmean shiftBloodCellsCluster analysisClustering algorithmsCytologyDiagnosisDiseasesLearning systemsPattern recognitionAcute leukemiaclusteringClustering techniquesK - means clusteringK-meansMean shiftMean shift algorithmSegmentation techniquesImage segmentationConference Paper3733786836609