Automation of differential blood count

A technique for automating the differential count of blood is presented. The proposed system takes as input, color images of stained peripheral blood smears and identi-fies the class of each of the White Blood Cells (WBC), in order to determine the count of cells in each class. The pro-cess involves segmentation, feature extraction and classifica-tion. WBC segmentation is a two-step process carried out on the HSV-equivalent of the image, using K-Means clus-tering followed by EM-algorithm. Features extracted from the segmented cytoplasm and nucleus, are motivated by the visual cues of shape, color and texture. Various classifiers have been explored on 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 50 patterns, with about 10 samples from each class, is used. The test data, disjoint form the training set, consists of 34 patterns, fairly represented by every class. The best classification ac-curacy of 97% is obtained using Neural networks, followed by 94% using SVM.