2D and 3D Vascular Structures Enhancement via Multiscale Fractional Anisotropy Tensor

Haifa F. Alhasson, Shuaa S. Alharbi, Boguslaw Obara

ECCVW 2018 pp. 365-374

doi:10.1007/978-3-030-11024-6_26 /eccvw/2018/alhasson2018eccvw-2d/

Abstract

The detection of vascular structures from noisy images is a fundamental process for extracting meaningful information in many applications. Most well-known vascular enhancing techniques often rely on Hessian-based filters. This paper investigates the feasibility and deficiencies of detecting curve-like structures using a Hessian matrix. The main contribution is a novel enhancement function, which overcomes the deficiencies of established methods. Our approach has been evaluated quantitatively and qualitatively using synthetic examples and a wide range of real 2D and 3D biomedical images. Compared with other existing approaches, the experimental results prove that our proposed approach achieves high-quality curvilinear structure enhancement.

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Cite

Text

Alhasson et al. "2D and 3D Vascular Structures Enhancement via Multiscale Fractional Anisotropy Tensor." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11024-6_26

Markdown

[Alhasson et al. "2D and 3D Vascular Structures Enhancement via Multiscale Fractional Anisotropy Tensor." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/alhasson2018eccvw-2d/) doi:10.1007/978-3-030-11024-6_26

BibTeX

@inproceedings{alhasson2018eccvw-2d,
  title     = {{2D and 3D Vascular Structures Enhancement via Multiscale Fractional Anisotropy Tensor}},
  author    = {Alhasson, Haifa F. and Alharbi, Shuaa S. and Obara, Boguslaw},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2018},
  pages     = {365-374},
  doi       = {10.1007/978-3-030-11024-6_26},
  url       = {https://mlanthology.org/eccvw/2018/alhasson2018eccvw-2d/}
}