PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Steerable Features for Statistical 3D Dendrite Detection,
German Gonzalez, Francois Aguet, Francois Fleuret, Michael Unser and Pascal Fua
In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)(2009).


Most state-of-the-art algorithms for filament detection in 3­D 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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:6245
Deposited By:Francois Fleuret
Deposited On:08 March 2010