Aims:　The　proposed　method　falls　into　the　category　of　medical　image　processing.　Background:　Computer-aided　automatic　analysis　systems　for　the　analysis　and　cytometry　of　leukocyte　(White　Blood　Cells,　WBCs)　in　human　blood　smear　images　are　a　powerful　diagnostic　tool　for　many　types　of　diseases,　such　as　anemia,　malaria,　syphilis,　heavy　metal　poisoning,　and　leukemia.　Leukocyte　segmentation　is　a　basis　of　its　automatic　analysis,　and　the　segmentation　accuracy　will　directly　influence　the　reliability　of　image-based　automatic　leukocyte　analysis.　Objective:　This　paper　aims　to　present　a　leukocyte　segmentation　method,　which　improves　segmentation　accuracy　under　rapid　and　standard　staining　conditions.　Methods:　The　proposed　method　first　localizes　leukocytes　by　color　component　combination　and　Adaptive　Histogram　Thresholding　(AHT),　and　crops　sub-image　corresponding　to　each　leukocyte.　Then,　the　proposed　method　employs　AHT　to　extract　the　nucleus　of　leukocyte　and　utilizes　image　color　features　to　remove　image　backgrounds　such　as　red　blood　cells　and　dyeing　impurities.　Finally,　Canny　edge　detection　is　performed　to　extract　the　entire　leukocyte.　Accordingly,　cytoplasm　is　obtained　by　subtracting　nucleus　with　leukocyte.　Results:　Experimental　results　on　two　datasets　containing　160　leukocyte　images　show　that　the　proposed　method　obtains　more　accurate　segmentation　results　than　their　counterparts.　Conclusion:　The　proposed　method　obtains　more　accurate　segmentation　results　than　their　counterparts　under　rapid　and　standard　staining　conditions.