A Simple and Accurate Method for White Blood Cells Segmentation Using K-Means Algorithm

Abstract

White Blood Cells (WBCs) counting provides invaluable information for diagnosis of different disease. Automatic counting is helpful for improving the hematological procedure. First step in automation; segmentation; is crucial for subsequent steps; feature extraction and classification. In this paper, WBCs segmentation using K-means Clustering (KMC) is proposed. First, RGB image is converted to L*a*b*. Next, data in a* and b* are fed to KMC with proper Initial Seed Points (ISP) to extract nuclei. Then, nuclei are subtracted from prime image and data in L* is fed to KMC with suitable ISP to estimate the background. Next, both nuclei and background are subtracted from prime image and residual image is enhanced and converted to L* a* b*. Next, data in b* are fed to KMC with appropriate ISP to segment cytoplasm and finally entire cell. We achieved an average of 6.46 Segmentation Error and 93.71 Jaccard Similarity Index which are desired in segmentation.