Segmentation of white blood cells from microscopic images using K-means clustering

In this paper, a new segmentation scheme for the white blood cells from microscopic images is proposed. The method is based on the K-means clustering technique. The RGB test images are converted to the L*a*b color space, and then the two color components (a and b) are used as features to the K-means clustering algorithm. The proposed method is tested and evaluated using blood cell images from publicly available dataset. Experiments demonstrate that the proposed method performs well and able to segment white blood cells from microscopic images.