Extracting 3D Parametric Curves from 2D Images of Helical Objects

Abstract

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Reconstructing a 3D helical 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.

Publication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Chas Nelson
Chas Nelson
LKAS Research Fellow in Data Science

Interdisciplinary scientist with a background in bioimaging and informatics.

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