A Model-Based Approach for Automated Feature Extraction in Fundus Images
Abstract
A new approach to automatically extract the main features in color fundus images is proposed. The optic disk is localized by principal component analysis (PCA) and its shape is detected by a modified active shape model (ASM). Exudates are extracted by the combined region growing and edge detection. A fundus coordinate system is further set up based on fovea localization to provide a better description of the features in fundus images. The success rates achieved are 99%, 94%, and 100% for disk localization, disk boundary detection, and fovea localization respectively. The sensitivity and specificity for exudate detection are 100% and 71%. The success of the proposed algorithms can be attributed to utilization of the model-based methods.
Cite
Text
Li and Chutatape. "A Model-Based Approach for Automated Feature Extraction in Fundus Images." IEEE/CVF International Conference on Computer Vision, 2003. doi:10.1109/ICCV.2003.1238371Markdown
[Li and Chutatape. "A Model-Based Approach for Automated Feature Extraction in Fundus Images." IEEE/CVF International Conference on Computer Vision, 2003.](https://mlanthology.org/iccv/2003/li2003iccv-model/) doi:10.1109/ICCV.2003.1238371BibTeX
@inproceedings{li2003iccv-model,
title = {{A Model-Based Approach for Automated Feature Extraction in Fundus Images}},
author = {Li, Huiqi and Chutatape, Opas},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2003},
pages = {394-399},
doi = {10.1109/ICCV.2003.1238371},
url = {https://mlanthology.org/iccv/2003/li2003iccv-model/}
}