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Microscope-based white blood cell classification plays an important role in diagnosing disease. The number of segments of nucleus and the shape of segments of nucleus are regarded as important features. Since it is difficult to automatically extract these features from a blood smeared image, they have not been used in the current automatic classifiers based on smeared images. In this paper, an approach based on the Poisson equation is proposed to extract the number of segments of nucleus in a more straightforward manner, and inner distances are used to represent the shape features of the nucleus segments. The experimental results show that the proposed approaches can extract the features efficiently. These important features can be added to the input feature set of neural networks or other classifiers to improve classification results of leukocytes in a blood smeared image.