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Sensors 2014, 14(9), 16128-16147; doi:10.3390/s140916128

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

1
School of Information Science and Engineering, Shandong University, Jinan 250100, China
2
Department of nephrology, Qilu Hospital of Shandong University, Jinan 250012, China
3
Department of Oncology, the Second Hospital of Shandong University, Jinan 250100, China
4
Department of Hematology, the Second Hospital of Shandong University, Jinan 250100, China
*
Authors to whom correspondence should be addressed.
Received: 12 May 2014 / Revised: 10 July 2014 / Accepted: 24 July 2014 / Published: 1 September 2014
(This article belongs to the Section Biosensors)
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Abstract

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. View Full-Text
Keywords: white blood cell; segmentation; color space decomposition; k-means clusters white blood cell; segmentation; color space decomposition; k-means clusters
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

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.

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