ridgedetection

Ridgedetection, also known as ridge detection, is the process of identifying ridge-like structures in digital images. Ridges are elongated, curvilinear features that often correspond to vessels, fibers, road networks, or fingerprint lines. The goal is to produce a representation of ridges—often as a centerline skeleton or a ridge strength map—that supports subsequent analysis such as tracking, matching, or segmentation.

Many detectors rely on local image derivatives. Hessian-based methods examine the eigenvalues of the second-derivative (Hessian) matrix to highlight elongated structures, while ridge filters combine first- and second-order derivatives to produce a ridge strength score. Multi-scale approaches assess ridge presence across widths, improving robustness to varying ridge thickness. Vesselness or tubularity filters (for example Frangi-type methods) are widely used in medical imaging, and Steger’s method targets ridge centerlines via zero-crossings and non-maximum suppression along ridge normals.

Typical processing steps include smoothing to reduce noise, computing derivative-based responses, estimating ridge orientation, performing non-maximum suppression along the local ridge direction, and thresholding or skeletonization to obtain a final ridge map.

Applications cover medical imaging (retinal vasculature, neural fibers), fingerprint analysis, remote sensing (road extraction, river networks), and texture analysis. Evaluation uses ground-truth annotations and metrics such as precision, recall, F1 score, and ridge-length or skeleton accuracy.

Challenges include noise and low contrast, variability in ridge width, ridge crossings, and computational cost for multi-scale methods. Recent trends combine classical detectors with learning-based models to improve robustness and accuracy.