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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
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author Zhang, Congcong
Xiao, Xiaoyan
Li, Xiaomei
Chen, Ying-Jie
Zhen, Wu
Chang, Jun
Zheng, Chengyun
Liu, Zhi
author_facet Zhang, Congcong
Xiao, Xiaoyan
Li, Xiaomei
Chen, Ying-Jie
Zhen, Wu
Chang, Jun
Zheng, Chengyun
Liu, Zhi
author_sort Zhang, Congcong
collection PubMed
description 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.
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spelling pubmed-42081662014-10-24 White Blood Cell Segmentation by Color-Space-Based K-Means Clustering Zhang, Congcong Xiao, Xiaoyan Li, Xiaomei Chen, Ying-Jie Zhen, Wu Chang, Jun Zheng, Chengyun Liu, Zhi Sensors (Basel) Article 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. MDPI 2014-09-01 /pmc/articles/PMC4208166/ /pubmed/25256107 http://dx.doi.org/10.3390/s140916128 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Zhang, Congcong
Xiao, Xiaoyan
Li, Xiaomei
Chen, Ying-Jie
Zhen, Wu
Chang, Jun
Zheng, Chengyun
Liu, Zhi
White Blood Cell Segmentation by Color-Space-Based K-Means Clustering
title White Blood Cell Segmentation by Color-Space-Based K-Means Clustering
title_full White Blood Cell Segmentation by Color-Space-Based K-Means Clustering
title_fullStr White Blood Cell Segmentation by Color-Space-Based K-Means Clustering
title_full_unstemmed White Blood Cell Segmentation by Color-Space-Based K-Means Clustering
title_short White Blood Cell Segmentation by Color-Space-Based K-Means Clustering
title_sort white blood cell segmentation by color-space-based k-means clustering
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208166/
https://www.ncbi.nlm.nih.gov/pubmed/25256107
http://dx.doi.org/10.3390/s140916128
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