Human leukocytes (white blood cells) can be divided into about twenty subclasses and the estimation of their distribution, called differential counting, is an important diagnostic tool in various clinical settings. Automatic differential counters based on digital image analysis require good segmentation algorithms to locate each cell and the accuracy of the subsequent classification depends on the correct segmentation of solitary cells as well as complex cell clusters.

Early leukocyte segmentation algorithms relied on various thresholding schemes to locate the nucleus and cytoplasm of solitary cells but could not handle clusters. Recently we described a complete segmentation procedure that solves the cluster-separation problem using moving interface models and a model-based combinatorial optimization scheme. In this paper, the algorithm is improved and its accuracy is evaluated.