Automated multi-stage segmentation of white blood cells via optimizing color processing

Segmentation of white blood cells (i.e. leukocytes) is a crucial step toward the development of haematological images analysis of peripheral blood smears. This step, however, is complicated by the complex nature of the different types of white blood cells and their large variations in shape, texture, color, and density. This study addresses this issue and presents a single fully automatic segmentation framework for both nuclei and cytoplasm of the five classes of leukocytes in a microscope blood smear. The proposed framework integrates a priori information of enhanced nuclei color with Gram-Schmidt orthogonalization, and multi-scale morphological enhancement to localize the nuclei, whereas clustering-based seed extraction and watershed are utilized to segment the cytoplasm. The experimental results on two different datasets show that the proposed method works successfully for both nuclei and cytoplasm segmentation, and achieves more accurate segmentation results compared to six leukocytes segmentation methods in the literature.