A novel region labeling method for blood cell images

Blood cell images are difficult for segmentation because of their intrinsic characteristics. We proposed a dual-layer structure to solve the problem: the 1st layer introduces and simplifies pulse coupled neural network to split the images based on traditional Split-Merge method, and Mumford-Shah model was utilized to merge the split areas. The output of the first layer is a coarse segmented image. The second layer extracts the edges via LoG operator with considering the edges whose length are larger than given threshold as cell edge while the opposite as false-edges caused by noises. The output is a non-continuous edge. To obtain the final labeling results, we combined the two layers together with skeleton extraction and thinning from mathematical morphology. Experiments suggested the superiority of proposed method.