Abstract: This paper presents a novel method for segmentation of white blood cells (WBCs) in peripheral blood and bone marrow images under different lights through mean shift clustering, color space conversion and nucleus mark watershed operation (NMWO). The proposed method focuses on obtaining seed points. First, color space transformation and image enhancement techniques are used to obtain nucleus groups as inside seeds. Second, mean shift clustering, selection of the C channel component in the CMYK model, and illumination intensity adjustment are employed to acquire WBCs as outside seeds. Third, the seeds and NMWO are employed to precisely determine WBCs and solve the cell adhesion problem. Morphological operations are further used to improve segmentation accuracy. Experimental results demonstrate that the algorithm exhibits higher segmentation accuracy and robustness compared with traditional methods.  OPEN ACCESS
Liu et al. REF performed segmentation of white blood cells using mean shift clustering and watershed operation on 306 images collected from Hospital of Shandong University.
Segmentation of White Blood Cells through Nucleus Mark Watershed Operations and Mean Shift Clustering
Sensors
Sensors
Engineering Computer Science Medicine