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Proceedings Paper

Detection of curvilinear objects in biological noisy image using feature-adapted fast slant stack
Author(s): Sylvain Berlemont; Aaron Bensimon; Jean-Christophe Olivo-Marin
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Paper Abstract

This paper presents a new method for computing the Feature-adapted Radon and Beamlet transforms [1] in a fast and accurate way. These two transforms can be used for detecting features running along lines or piecewise constant curves. The main contribution of this paper is to unify the Fast Slant Stack method, introduced in [2], with linear filtering technique in order to define what we call the Feature-adapted Fast Slant Stack. If the desired feature detector is chosen to belong to the class of steerable filters, our method can be achieved in O(N log(N)), where N = n2 is the number of pixels. This new method leads to an efficient implementation of both Feature-adapted Radon and Beamlet transforms, that outperforms our previous works [1] both in terms of accuracy and speed. Our method has been developed in the context of biological imaging to detect DNA filaments in fluorescent microscopy.

Paper Details

Date Published: 27 September 2007
PDF: 9 pages
Proc. SPIE 6701, Wavelets XII, 67010H (27 September 2007); doi: 10.1117/12.733619
Show Author Affiliations
Sylvain Berlemont, Institut Pasteur (France)
Univ. of Paris V (France)
Genomic Vision (France)
Aaron Bensimon, Genomic Vision (France)
Jean-Christophe Olivo-Marin, Institut Pasteur (France)

Published in SPIE Proceedings Vol. 6701:
Wavelets XII
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

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