Segmentation of dense leukocyte clusters

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 cell clusters. Previously published segmentation algorithms mainly use various thresholding schemes to extract the nucleus and cytoplasm of solitary cells but, so far, no successful cluster segmentation method has been developed. In this paper we present a model-based segmentation algorithm that uses interface propagation models to locate nuclear segments and their adherent cytoplasms. These segments are then assembled using a model-based combinatorial optimization scheme. The results are very promising and, to our knowledge, this is the first successful attempt to solve this problem.