Leukocyte segmentation and SVM classification in blood smear images
Machine Graphics & Vision International Journal
Leukocyte segmentation in blood smear images using region-based active contours
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Leukocyte detection using nucleus contour propagation
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
White blood cell segmentation and classification in microscopic bone marrow images
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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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.