Patch-Based White Blood Cell Nucleus Segmentation Using Fuzzy Clustering

Cell segmentation is one of important steps in the automatic white blood cell differential counting. In this paper, we propose a technique to segment singlecell images of white blood cells in bone marrow into two regions, i.e., nucleus and non-nucleus. The segmentation is based on the fuzzy C-means clustering and mathematical morphology. The segmentation results are compared to an expert’s manually segmented images. The initial investigation of the use of the derived segmented images in the cell classification is also performed by using the Bayes classifier.