Segmentation and classification of white blood cells

Automated medical image processing and analysis offers a powerful tool for medical diagnosis. In this work we tackle the problem of white blood cell shape analysis based on the morphological characteristics of their outer contour and nuclei. The paper presents a set of preprocessing and segmentation algorithms along with a set of features that are able to recognize and classify different categories of normal white blood cells. The system was tested on gray level images obtained from a CCD camera through a microscope and produced a correct classification rate close to 91%.