Leukocyte image segmentation acts as the foundation for all automated image based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Even though much effort has been put in developing suitable segmentation routines, the problem still remains open in areas like pathological imaging. Clustering is an essential image segmentation procedure which segments an image into desired regions. This paper introduces a novel Shadowed C-means (SCM) clustering approach towards leukocyte segmentation in blood microscopic images. The segmented nucleus and cytoplasm of a leukocyte can be used for feature extraction which can lead to acute leukemia detection. Absence of parameter tuning in SCM with acceptable segmentation performance gives the proposed scheme an edge over standard cluster based segmentation techniques. Comparative analysis reveals that the proposed algorithm is fast and robust in segmenting stained blood microscopic images in the presence of outliers.