Blood tests are of the most important and counting of leukocytes in peripheral blood is commonly used in basic clinical diagnosis. A major requirement for this paper is an efficient method to segment cell images. This work presents an accurate segmentation method for automatic count of white blood cells. First a simple thresholding approach is applied to give initial labels to pixels in the blood cell images. The algorithm is based on information about blood smear images, and then the labels are adjusted with a shape detection method based on large regional context information to produce meaningful results. This approach makes use of knowledge of blood cell structure, the experimental result shows that this method is more powerful than traditional methods that use only local context information. It can perform accurate segmentation of white blood cells even if they have unsharp boundaries.

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