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

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 cytom...

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Detalles Bibliográficos
Autores principales: Zhang, Congcong, Xiao, Xiaoyan, Li, Xiaomei, Chen, Ying-Jie, Zhen, Wu, Chang, Jun, Zheng, Chengyun, Liu, Zhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208166/
https://www.ncbi.nlm.nih.gov/pubmed/25256107
http://dx.doi.org/10.3390/s140916128
Descripción
Sumario: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.