White Blood Cell Segmentation by Color-Space-Based K-Means Clustering

• Published in: Sensors (Basel, Switzerland) Vol. 14; no. 9; pp. 16128 - 16147
• Main Authors: Zhang, Congcong, Xiao, Xiaoyan, Li, Xiaomei, Chen, Ying-Jie, Zhen, Wu, Chang, Jun, Zheng, Chengyun, Liu, Zhi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.09.2014; MDPI
• Subjects: Accuracy; Algorithms; Blood; Cluster Analysis; Color; color space decomposition; Cytoplasm; Humans; Image Cytometry; Image Processing, Computer-Assisted; Immune system; k-means clusters; Leukocyte Count; Leukocytes; Leukocytes - cytology; Methods; Morphology; Performance evaluation; segmentation; Sensors; Support vector machines; white blood cell; China
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s140916128

White blood cell (WBC) segmentation, which is important for cytometry, is a challenging issue because of the morphological diversity of WBCs and the complex and uncertain background of blood smear images. This paper proposes a novel method for the nucleus and cytoplasm segmentation of WBCs for cytometry. A color adjustment step was also introduced before segmentation. Color space decomposition and k-means clustering were combined for segmentation. A database including 300 microscopic blood smear images were used to evaluate the performance of our method. The proposed segmentation method achieves 95.7% and 91.3% overall accuracy for nucleus segmentation and cytoplasm segmentation, respectively. Experimental results demonstrate that the proposed method can segment WBCs effectively with high accuracy.