27 September 2007 Detection of curvilinear objects in biological noisy image using feature-adapted fast slant stack
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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.
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Sylvain Berlemont, Aaron Bensimon, Jean-Christophe Olivo-Marin, "Detection of curvilinear objects in biological noisy image using feature-adapted fast slant stack", Proc. SPIE 6701, Wavelets XII, 67010H (27 September 2007); doi: 10.1117/12.733619; https://doi.org/10.1117/12.733619
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