Automatic Blood Cell Segmentation Using K-Mean Clustering from Microscopic Thin Blood Images

Abstract

Blood cell segmentation is a critical innovation for differential blood count, and parasitic disease identification such as malaria, Babesiosis, Chagas etc. In many parasitic diseases parasites infect blood cells. In sickle cell anemia blood cells segmentation is important to know the morphology of Red Blood Cells (RBCs). This paper proposed a method of an automatic blood cells segmentation using K-Mean clustering. Giemsa stained thin blood slides are used for image acquisition by high resolution camera. Processing includes preprocessing, segmentation, separation of overlapped blood cells and evaluation of segmentation results. Proposed algorithm is tested on 60 images. Database images used are of different magnification and surrounding conditions. Correct segmentation accuracy achieved is 98.89%.