Automatic localization and feature extraction of white blood cells

The paper presents a method for automatic localization and feature extraction of white blood cells (WBCs) with color images to develop an efficient automated WBC counting system based on image analysis and recognition. Nucleus blobs extraction consists of five steps: (1) nucleus pixel labeling; (2) filtration of nucleus pixel template; (3) segmentation and extraction of nucleus blobs by region growing; (4) removal of uninterested blobs; and (5) marking of external and internal blob border, and holes pixels. The detection of nucleus pixels is based on the intensity of the G image plane and the balance between G and B intensity. Localized nucleus segments are grouped into a cell nucleus by a hierarchic merging procedure in accordance with their area, shapes and conditions of their spatial occurrence. Cytoplasm segmentation based on the pixel intensity and color parameters is found to be unreliable. We overcome this problem by using an edge improving technique. WBC templates are then calculated and additional cell feature sets are constructed for the recognition. Cell feature sets include description of principal geometric and color properties for each type of WBCs. Finally we evaluate the recognition accuracy of the developed algorithm that is proved to be highly reliable and fast.