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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| Published in | Sensors (Basel, Switzerland) Vol. 14; no. 9; pp. 16128 - 16147 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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01.09.2014
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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. |
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| AbstractList | 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. 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.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. |
| Author | Zhang, Congcong Zhen, Wu Liu, Zhi Li, Xiaomei Zheng, Chengyun Chang, Jun Xiao, Xiaoyan Chen, Ying-Jie |
| AuthorAffiliation | 1 School of Information Science and Engineering, Shandong University, Jinan 250100, China; E-Mails: zhangcongcong@mail.sdu.edu.cn (C.Zha.); changjun@sdu.edu.cn (J.C.) 4 Department of Hematology, the Second Hospital of Shandong University, Jinan 250100, China 2 Department of nephrology, Qilu Hospital of Shandong University, Jinan 250012, China; E-Mail: xiaoyanxiao2007@163.com 3 Department of Oncology, the Second Hospital of Shandong University, Jinan 250100, China; E-Mails: sdulixiaomei@163.com (X.L.); lz505@163.com (Y.-J.C.); qinghuanjn@gmail.com (W.Z.) |
| AuthorAffiliation_xml | – name: 4 Department of Hematology, the Second Hospital of Shandong University, Jinan 250100, China – name: 3 Department of Oncology, the Second Hospital of Shandong University, Jinan 250100, China; E-Mails: sdulixiaomei@163.com (X.L.); lz505@163.com (Y.-J.C.); qinghuanjn@gmail.com (W.Z.) – name: 2 Department of nephrology, Qilu Hospital of Shandong University, Jinan 250012, China; E-Mail: xiaoyanxiao2007@163.com – name: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, China; E-Mails: zhangcongcong@mail.sdu.edu.cn (C.Zha.); changjun@sdu.edu.cn (J.C.) |
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| SubjectTerms | Accuracy Algorithms Blood Cluster Analysis Color color space decomposition Cytoplasm Humans Image Cytometry Image Processing, Computer-Assisted Immune system k-means clusters Leukocyte Count Leukocytes Leukocytes - cytology Methods Morphology Performance evaluation segmentation Sensors Support vector machines white blood cell |
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| Title | White Blood Cell Segmentation by Color-Space-Based K-Means Clustering |
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