Abstract
Automated identification of cell cycle phases captured via fluorescent microscopy technique is very important for cell cycle understanding and drug discovery. In this paper, we propose a novel cell detection method that utilizes both the intensity and shape information of cell to improve the segmentation quality. In contrast to conventional off-line learning algorithms for classifcation, our study necessitates the on-line adaptivity to accommodate the ever-changing experimental conditions. An Online Support Vector Classifier (OSVC) is thus proposed, which features the removal of support vectors from the old model and assigning the new training examples with different weights according to their importance. Experimental results show the proposed system is effective for cell imaging segmentation and cell phase identification in time-lapse microscopy.
Original language | English (US) |
---|---|
Title of host publication | 2007 4th IEEE International Symposium on Biomedical Imaging |
Subtitle of host publication | From Nano to Macro - Proceedings |
Pages | 65-68 |
Number of pages | 4 |
DOIs | |
State | Published - Nov 27 2007 |
Event | 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States Duration: Apr 12 2007 → Apr 15 2007 |
Other
Other | 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 |
---|---|
Country/Territory | United States |
City | Arlington, VA |
Period | 4/12/07 → 4/15/07 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Medicine(all)