Segmenting Cell Images: A Deterministic Relaxation Approach
Abstract
Automatic segmentation of digital cell images into four regions, namely nucleus, cytoplasm, red blood cell (rbc), and background, is an important step for pathological measurements. Using an adaptive thresholding of the histogram, the cell image can be roughly segmented into three regions: nucleus, a mixture of cytoplasm and rbc’s, and background. This segmentation is served as an initial segmentation for our iterative image segmentation algorithm. Specifically, MAP (maximum a posteriori) criterion formulated by the Bayesian framework with the original image data and local variance image field (LVIF) is used to update the class labels iteratively by a deterministic relaxation algorithm. Finally, we draw a line to separate the touching rbc from the cytoplasm.
Cite
Text
Won et al. "Segmenting Cell Images: A Deterministic Relaxation Approach." European Conference on Computer Vision, 2004. doi:10.1007/978-3-540-27816-0_24Markdown
[Won et al. "Segmenting Cell Images: A Deterministic Relaxation Approach." European Conference on Computer Vision, 2004.](https://mlanthology.org/eccv/2004/won2004eccv-segmenting/) doi:10.1007/978-3-540-27816-0_24BibTeX
@inproceedings{won2004eccv-segmenting,
title = {{Segmenting Cell Images: A Deterministic Relaxation Approach}},
author = {Won, Chee Sun and Nam, Jae Yeal and Choe, Yoonsik},
booktitle = {European Conference on Computer Vision},
year = {2004},
pages = {281-291},
doi = {10.1007/978-3-540-27816-0_24},
url = {https://mlanthology.org/eccv/2004/won2004eccv-segmenting/}
}