Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3390/s140916128 | SENSORS |
Keywords | Field | DocType |
white blood cell,segmentation,color space decomposition,k-means clusters | k-means clustering,Computer vision,Color space,Biology,Segmentation,Artificial intelligence,Image Cytometry,Cluster analysis,White blood cell,Cytometry | Journal |
Volume | Issue | ISSN |
14 | 9.0 | 1424-8220 |
Citations | PageRank | References |
8 | 0.72 | 6 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Congcong Zhang | 1 | 8 | 0.72 |
| Xiaoyan Xiao | 2 | 16 | 1.64 |
| Xiaomei Li | 3 | 16 | 1.65 |
| Ying-Jie Chen | 4 | 8 | 0.72 |
| Wu Zhen | 5 | 8 | 0.72 |
| Jun Chang | 6 | 15 | 1.29 |
| Chengyun Zheng | 7 | 15 | 1.29 |
| Zhi Liu | 8 | 23 | 14.28 |