We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
White Blood Cell Segmentation by Color-Space-Based K-Means Clustering.
- Authors
Congcong Zhang; Xiaoyan Xiao; Xiaomei Li; Ying-Jie Chen; Wu Zhen; Jun Chang; Chengyun Zheng; Zhi Liu
- 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.
- Subjects
LEUCOCYTES; CYTOMETRY; K-means clustering; IMAGE segmentation; CLUSTER analysis (Statistics)
- Publication
Sensors (14248220), 2014, Vol 14, Issue 9, p16128
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s140916128