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. © 2009 Springer-Verlag.
CITATION STYLE
González, G., Aguet, F., Fleuret, F., Unser, M., & Fua, P. (2009). Steerable features for statistical 3D dendrite detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5762 LNCS, pp. 625–632). https://doi.org/10.1007/978-3-642-04271-3_76
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