  The detection of curvilinear structures is an important step for various
computer vision applications, ranging from medical image analysis for
segmentation of blood vessels, to remote sensing for the identification of
roads and rivers, and to biometrics and robotics, among others. %The visual
system of the brain has remarkable abilities to detect curvilinear structures
in noisy images. This is a nontrivial task especially for the detection of thin
or incomplete curvilinear structures surrounded with noise. We propose a
general purpose curvilinear structure detector that uses the brain-inspired
trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear
filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis
thresholding and morphological closing. We demonstrate its effectiveness on a
data set of noisy images with cracked pavements, where we achieve
state-of-the-art results (F-measure=0.865). The proposed method can be employed
in any computer vision methodology that requires the delineation of curvilinear
and elongated structures.
