Automatic Leukocyte Classification Based on Microscope Images

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 involves segmentation, feature extraction and classification. The segmentation procedure, a novel simple algorithm, is proposed for localization of white blood cells and the different cell components are separated with automatic thresholding. Features extracted from the segmented nucleus are motivated by the visual cues of shape, color and texture. This research uses the artificial neural network for implemention and uses the different combinations of feature sets. The results presented here are based on trials conducted with normal cells. For training the classifiers, a library set of 233 patterns is used. The tested data consists of 134 samples and produced correct classification rate close to 88.10 %.