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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2014
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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. |
format | Online Article Text |
id | pubmed-4208166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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