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

Research paper by Chris C Willcocks, Philip P Jackson, Carl C Nelson, Boguslaw B Obara

Indexed on: 24 Jan '17Published on: 24 Jan '17Published in: IEEE transactions on pattern analysis and machine intelligence



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.