White Blood Cell Classification based on the Combination of Eigen Cell and Parametric Feature Detection

Numbers of white blood cells in different classes help doctors to diagnose patients. A technique for automating the differential count of white blood cell is presented. The proposed system takes an input, color image of stained peripheral blood smears. The process in general involves segmentation, feature extraction and classification. In this paper, features extracted from the segmented cell are motivated by the concept of the well-known eigen face which is performed on the pre-classified which blood cell based on parametric feature detection. The derived eigen value and eigen vector contributes to the important feature in the classification process. The results presented here are based on trials conducted with normal cells. For training the classifiers, a library set of 50 patterns is used. The tested data consists of 50 samples and produced correct classification rate close to 92 %