Due　to　the　complexity　of　cell　structure　and　the　overlap　of　cells,　accurate　segmentation　of　cytoplasm　and　nucleus　remains　a　challenging　problem.　Here　an　algorithm　based　on　feature　weight　adaptive　K-means　clustering　to　extract　complex　leukocytes　is　proposed.　In　traditional　K-means　clustering　algorithm,　the　initial　clustering　center　is　randomly　assigned,　which　affects　the　clustering　effect.　In　this　paper,　the　initial　clustering　center　is　selected　according　to　the　histogram　distribution　of　cell　image,　which　not　only　improves　the　clustering　effect,　but　also　reduces　the　time　complexity　of　the　algorithm　from　O　(n)　to　O　(1).　Then,　the　improved　K-means　algorithm　can　have　some　anti-noise　performance　by　using　a　non-Euclidean　distanceBefore　leukocytes　are　extracted,　the　color　space　is　decomposed,　and　the　cell　nucleus　and　cytoplasm　are　extracted　according　to　the　color　component　and　the　improved　K-means　clustering　algorithm.　Color　space　decomposition　and　K-means　clustering　are　combined　for　segmentation.　Finally,　Adherent　leukocytes　are　separated　based　on　watershed　algorithm.　The　proposed　segmentation　method　achieves　95.81%　and　91.28%　overall　accuracy　for　nucleus　and　cytoplasm　segmentation,　respectively.　Experimental　results　show　that　the　new　method　can　effectively　segment　complex　leukocytes　and　have　high　accuracy.　©　2019　Computer　Society　of　the　Republic　of　China.　All　rights　reserved.