Automatic White Blood Cell Differential Classification a Thesis in Mechatronics

The characteristics (quantity, shape and color) of the white blood cell (WBC) can give vital information about a patient’s health. Hematologists, with the aid of microscopes, use their experience to classify WBCs and make appropriate reporting and recommendations to physicians. Automating the segmentation and classification of WBC could provide a useful tool in medical diagnoses. In this work, computer-based segmentation and classification of the four main classes of WBC (Neutrophils, Eosinophils, Lymphocytes, and Monocytes) were completed. Soft computing algorithms including neural network (NN) and polynomial classifiers (PC) were used for WBC classification, while watershed and thresholding based on size, shape, color and texture characteristics were used to segment WBC from Red Blood Cells RBC, platelets, cell fragments and stains. Furthermore, characteristics of the WBC were utilized in association with the intelligent systems to classify these WBC’s to different classes. The number and distribution of different classes of WBC has medical indications (e.g. high Neutrophils count may possibly imply cancer, whereas high Lymphocytes count could lead to AIDS). To classify WBC morphological based features, Discrete Cosine Transform (DCT) based features and Discrete Wavelets Transform (DWT) based features were used as input feature vectors to the Neural Networks NN and Polynomial Classifiers PC. Various