A novel decision-tree based classification of white blood cells

Automated medical image processing and analysis offer a powerful tool for medical diagnosis. In this work, a decision-tree based white blood cell (WBC) classification scheme for peripheral blood images is developed. Based on the sufficient analysis on the characteristics of white blood cells, 10 efficient features are extracted, including size, shape, intensity and color, and a classification scheme based on decision-tree is designed to classify 6 different types of normal white blood cells. Especially, an efficient approach to separate two types of neutrophil is presented. The presented scheme is tested on 59 WBCs coming from 3 sets of blood images, which are obtained under different dying and imaging conditions. Results show classification accuracy above 96%.