A novel framework for white blood cell segmentation based on stepwise rules and morphological features

Abstract

This study proposes a new white blood cell (WBC) segmentation method using region merging scheme and GVF (Gradient Vector Flow) snake. WBC segmentation consists of two schemes; nuclei segmentation and cytoplasm segmentation. For nuclei segmentation, we create a probability map using probability density function estimated from samples of WBC's nuclei and crop the sub-images to include nucleus by using the fact that nuclei have salient color against background and red blood cells. Then, mean-shift clustering is performed for region segmentation and merging rules are applied to merge particle clusters to nucleus. For cytoplasm segmentation, a hybrid approach is proposed that combines the spatial characteristics of cytoplasm and GVF snakes to delineate the boundary of the region of interest. Unlike previous algorithms, the main contribution of this study is to improve the accuracy of WBC segmentation and reduce the computational time by cropping sub-images and applying different segmentation rules according to the parts of cell. The evaluation of proposed method was performed on five WBC types and it showed that the proposed algorithm produced accurate segmentation results in most types of WBCs.