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Extracting 3D parametric curves from 2D images of helical objects

Willcocks, Chris; Jackson, Philip T.G.; Nelson, Carl J.; Obara, Boguslaw

Extracting 3D parametric curves from 2D images of helical objects Thumbnail


Philip T.G. Jackson

Carl J. Nelson

Boguslaw Obara


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.


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.

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


Accepted Journal Article (16.8 Mb)

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