Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction

TLDR

Centerline extraction of tubular objects is essential in many clinical image analyses, commonly performed via intensity ridge traversal. This study evaluates how initialization, noise, and singularities affect intensity ridge traversal and proposes multiscale heuristics and optimal‑scale measures to mitigate these effects. Monte Carlo experiments on simulated and clinical data assess how dynamic‑scale enhancements improve speed, accuracy, and automation. Dynamic‑scale ridge traversal is robust to initial parameters, incurs minimal computational overhead, achieves sub‑voxel accuracy, traverses branch points, tolerates significant noise, and demonstrates utility across diverse tubular structures in multiple organs and imaging modalities.

Abstract

The extraction of the centerlines of tubular objects in two and three-dimensional images is a part of many clinical image analysis tasks. One common approach to tubular object centerline extraction is based on intensity ridge traversal. In this paper, we evaluate the effects of initialization, noise, and singularities on intensity ridge traversal and present multiscale heuristics and optimal-scale measures that minimize these effects. Monte Carlo experiments using simulated and clinical data are used to quantify how these "dynamic-scale" enhancements address clinical needs regarding speed, accuracy, and automation. In particular, we show that dynamic-scale ridge traversal is insensitive to its initial parameter settings, operates with little additional computational overhead, tracks centerlines with subvoxel accuracy, passes branch points, and handles significant image noise. We also illustrate the capabilities of the method for medical applications involving a variety of tubular structures in clinical data from different organs, patients, and imaging modalities.