Steerable Features for Statistical 3D Dendrite Detection

Authors:
Germán González;François Aguet;François Fleuret;Michael Unser;Pascal Fua
Affiliations:
Computer Vision Lab, Ecole Polytechnique Fédérale de Lausanne, Switzerland;Biomedical Imaging Group, Ecole Polytechnique Fédérale de Lausanne, Switzerland;Computer Vision Lab, Ecole Polytechnique Fédérale de Lausanne, Switzerland and Idiap Research Institute, Martigny, Switzerland;Biomedical Imaging Group, Ecole Polytechnique Fédérale de Lausanne, Switzerland;Computer Vision Lab, Ecole Polytechnique Fédérale de Lausanne, Switzerland
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

Most state-of-the-art algorithms for filament detection in 3-D image-stacks rely on computing the Hessian matrix around individual pixels and labeling these pixels according to its eigenvalues. This approach, while very effective for clean data in which linear structures are nearly cylindrical, loses its effectiveness in the presence of noisy data and irregular structures. In this paper, we show that using steerable filters to create rotationally invariant features that include higher-order derivatives and training a classifier based on these features lets us handle such irregular structures. This can be done reliably and at acceptable computational cost and yields better results than state-of-the-art methods.