White Blood Cell Segmentation and Classification in Microscopic Bone Marrow Images

Abstract

An automatic segmentation technique for microscopic bone marrow white blood cell images is proposed in this paper. The segmentation technique segments each cell image into three regions, i.e., nucleus, cytoplasm, and background. We evaluate the segmentation performance of the proposed technique by comparing its results with the cell images manually segmented by an expert. The probability of error in image segmentation is utilized as an evaluation measure in the comparison. From the experiments, we achieve good segmentation performances in the entire cell and nucleus segmentation. The six-class cell classification problem is also investigated by using the automatic segmented images. We extract four features from the segmented images including the cell area, the peak location of pattern spectrum, the first and second granulometric moments of nucleus. Even though the boundaries between cell classes are not well-defined and there are classification variations among experts, we achieve a promising classification performance using neural networks with five-fold cross validation.