Segmentation technique of complex image scene for an automatic blood-cell-counting system

The paper presents a method for automatic localization and segmentation of white blood cells (WBCs) with color images to develop an efficient automated leukocyte counter by using pattern recognition-based slide readers. The segmentation techniques consist of the following steps. On the first a smear image acquired at the low magnification. The next is extraction of WBC nuclei by chromatic properties and image mapping. After this the cells clustered according to the distances between them and regions of interest (ROI) determined. Image of ROI captured at the high magnification and its validity checked. Then nucleus segments extracted and grouped into prospective cells. The detection of blood cells is based on the intensity of G image plane and the balance between G and B intensity of the nuclei. A cytoplasm region approximated by a circle area around the nucleus center. Finally, the cytoplasm area cleaned considering a priori knowledge of background color and possible cell occlusions. The result of the segmentation is presented in the form of a cell location list and image template in which every pixel is assigned to a label such as Background, Cytoplasm, Nucleus, Hole, etc. The proposed technique has yielded correct segmentation of complex image scenes for blood smears prepared by ordinary manual staining methods in 99% of tested images.