Evaluation of Blood Cell Segmentation Using Level Set-Based Force in Normal Direction via Edge Detection of Cell Shape

Segmentation of blood cell considers a gold standard key in diagnosing blood disease and producing automatic reports to haematologists and doctors. Blood cell segmentation is a challenging task due to non-illumination level, high number of overlapped cells per image, variations in cell densities among platelets, white blood cells and red blood cells, and the variety of staining process. In this paper, level set-based force in normal direction via edge detection of cell shape is proposed, for first time, to segment and split overlapped blood cell components, and deal with these challenges. The proposed method has used publicly CellaVision DM96 dataset. It is also compared with other existing segmentation methods. The result has shown that this method reduces the effects non-illumination and variation in cell densities. An extensive experiment demonstrates that the proposed method performance is superior against all other existing blood cell segmentation approaches except for one method in this paper which is slightly better than our proposed method. The result also shows that the proposed method can support pathology practice in the diagnosis of autoimmune diseases in the future.