White Blood Cell Segmentation by Color-Space-Based K-Means Clustering
AbstractWhite 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. View Full-Text
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Zhang, C.; Xiao, X.; Li, X.; Chen, Y.-J.; Zhen, W.; Chang, J.; Zheng, C.; Liu, Z. White Blood Cell Segmentation by Color-Space-Based K-Means Clustering. Sensors 2014, 14, 16128-16147.
Zhang C, Xiao X, Li X, Chen Y-J, Zhen W, Chang J, Zheng C, Liu Z. White Blood Cell Segmentation by Color-Space-Based K-Means Clustering. Sensors. 2014; 14(9):16128-16147.Chicago/Turabian Style
Zhang, Congcong; Xiao, Xiaoyan; Li, Xiaomei; Chen, Ying-Jie; Zhen, Wu; Chang, Jun; Zheng, Chengyun; Liu, Zhi. 2014. "White Blood Cell Segmentation by Color-Space-Based K-Means Clustering." Sensors 14, no. 9: 16128-16147.