Detecting Irregular Curvilinear Structures in Gray Scale and Color Imagery Using Multi-Directional Oriented Flux
Abstract
We propose a new approach to detecting irregular curvilinear structures in noisy image stacks. In contrast to earlier approaches that rely on circular models of the crosssections, ours allows for the arbitrarily-shaped ones that are prevalent in biological imagery. This is achieved by maximizing the image gradient flux along multiple directions and radii, instead of only two with a unique radius as is usually done. This yields a more complex optimization problem for which we propose a computationally efficient solution. We demonstrate the effectiveness of our approach on a wide range of challenging gray scale and color datasets and show that it outperforms existing techniques, especially on very irregular structures.
Cite
Text
Turetken et al. "Detecting Irregular Curvilinear Structures in Gray Scale and Color Imagery Using Multi-Directional Oriented Flux." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.196Markdown
[Turetken et al. "Detecting Irregular Curvilinear Structures in Gray Scale and Color Imagery Using Multi-Directional Oriented Flux." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/turetken2013iccv-detecting/) doi:10.1109/ICCV.2013.196BibTeX
@inproceedings{turetken2013iccv-detecting,
title = {{Detecting Irregular Curvilinear Structures in Gray Scale and Color Imagery Using Multi-Directional Oriented Flux}},
author = {Turetken, Engin and Becker, Carlos and Glowacki, Przemyslaw and Benmansour, Fethallah and Fua, Pascal},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.196},
url = {https://mlanthology.org/iccv/2013/turetken2013iccv-detecting/}
}