Unsupervised ridge detection using second order anisotropic Gaussian kernels

We propose the use of the second derivative of Anisotropic Gaussian Kernels for ridge detection. Such kernels, which have proven successful in edge and corner detection, offer interesting advantages over isotropic kernels. In the case of ridge detection, these advantages include the increase of the sensitivity at junctions, as well as an improved characterization of blob-like artefacts. We do not only illustrate these advantages on synthetic images, but also perform a comparison on a new dataset for line detection, which is composed of 100 images of in vitro fungi. (C) 2015 Elsevier B.V. All rights reserved.