title: Steerable features for statistical 3D dendrite detection.
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.
authors.affiliation: Computer Vision Lab, Ecole Polytechnique Fédérale de Lausanne, Switzerland. german.gonzalez@epfl.ch
authors.forename: German
authors.initials: G
authors.lastname: González
authors.forename: François
authors.initials: F
authors.lastname: Aguet
authors.forename: François
authors.initials: F
authors.lastname: Fleuret
authors.forename: Michael
authors.initials: M
authors.lastname: Unser
authors.forename: Pascal
authors.initials: P
authors.lastname: Fua
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D003712:Dendrites / Q000648:ultrastructure*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity