Name
Title | ||
|---|---|---|
Automatic working area classification in peripheral blood smears using spatial distribution features across scales |
Abstract | ||
|---|---|---|
Automatic classification of working areas in peripheral blood smears can provide objective and reproducible quality control for the evaluation of smears and smear maker devices. However, it has drawn little research attention. In this paper we study this topic using image analysis and statistical pattern recognition methods. We employ generic features without requiring the extraction of individual cells. Two new spatial distribution features across scales are defined and utilized to classify working areas. We demonstrate that the only feature and method proposed in a similar work by others is insufficient to characterize the goodness of working areas, particularly the cell distribution. However, by utilizing it together with the features developed in this paper, we can achieve much better results. Our method has been tested on about 150 labeled images acquired from three malaria-infected Giemsa-stained blood smears using an oil immersion 100x objective lens. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761743 | Tampa, FL |
Keywords | Field | DocType |
blood,cellular biophysics,medical image processing,pattern classification,statistical analysis,automatic working area classification,cell distribution,image analysis,individual cells extraction,peripheral blood smears,quality control,smear maker devices,smears evaluation,spatial distribution features,statistical pattern recognition | Kernel (linear algebra),Computer vision,Cellular biophysics,Pattern recognition,Computer science,Support vector machine,Oil immersion,Feature extraction,Artificial intelligence,Spatial distribution,Statistical analysis | Conference |
ISSN | ISBN | Citations |
1051-4651 E-ISBN : 978-1-4244-2175-6 | 978-1-4244-2175-6 | 0 |
PageRank | References | Authors |
0.34 | 1 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Xiong | 1 | 23 | 6.75 |
| Sim Heng Ong | 2 | 426 | 44.63 |
| Joo-Hwee Lim | 3 | 783 | 82.45 |
| N. N. Tung | 4 | 0 | 0.34 |
| Jiang Liu | 5 | 299 | 42.50 |
| Daniel Racoceanu | 6 | 198 | 24.30 |
| Kevin Tan | 7 | 28 | 2.82 |
| Alvin Chong | 8 | 0 | 0.34 |
| Kelvin Weng Chiong Foong | 9 | 78 | 8.21 |