Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake

► Propose two different schemes for segmenting the nuclei and cytoplasm of WBCs. ► For nuclei segmentation, a probability map is created using a probability density function estimated from samples of WBC's nuclei and sub-images cropped to include a nucleus based on the fact that nuclei have a salient color against the background and red blood cells. Mean-shift clustering is then performed for region segmentation, and a stepwise merging scheme applied to merge particle clusters with a nucleus. ► For cytoplasm segmentation, morphological opening is applied to a green image to boost the intensity of the granules and canny edges detected within the sub-image. The boundary edges and noise edges are then removed using removal rules, while a GVF snake is forced to deform to the cytoplasm boundary edges. ► The proposed method is demonstrated to improve the segmentation performance when compared to another method. ► Experimental results confirm that the proposed method can efficiently segment the nucleus and cytoplasm of WBCs, regardless of the WBC category.