Dr Chris Willcocks christopher.g.willcocks@durham.ac.uk
Associate Professor
Extracting 3D parametric curves from 2D images of helical objects
Willcocks, Chris; Jackson, Philip T.G.; Nelson, Carl J.; Obara, Boguslaw
Authors
Philip T.G. Jackson
Carl J. Nelson
Boguslaw Obara
Abstract
Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively.
Citation
Willcocks, C., Jackson, P. T., Nelson, C. J., & Obara, B. (2016). Extracting 3D parametric curves from 2D images of helical objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(9), 1757-1769. https://doi.org/10.1109/tpami.2016.2613866
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 20, 2016 |
Online Publication Date | Sep 26, 2016 |
Publication Date | Sep 26, 2016 |
Deposit Date | Sep 23, 2016 |
Publicly Available Date | Sep 23, 2016 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Print ISSN | 0162-8828 |
Electronic ISSN | 1939-3539 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 39 |
Issue | 9 |
Pages | 1757-1769 |
DOI | https://doi.org/10.1109/tpami.2016.2613866 |
Public URL | https://durham-repository.worktribe.com/output/1376088 |
Files
Accepted Journal Article
(16.8 Mb)
PDF
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
You might also like
Robust 3D U-Net Segmentation of Macular Holes
(2021)
Presentation / Conference Contribution
Segmentation of macular edema datasets with small residual 3D U-Net architectures
(2020)
Presentation / Conference Contribution
Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions
(2019)
Presentation / Conference Contribution
Coarse annotation refinement for segmentation of dot-matrix batchcodes
(2019)
Presentation / Conference Contribution
Style Augmentation: Data Augmentation via Style Randomization
(2019)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search