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

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.

Automatic Vascular Tree Formation Using the Mahalanobis Distance

“…Moreover, the radius ratio in the brain connected cases is close to one and as a small standard deviation compared to the radius ratio obtained for the liver datasets. In fact the segmentation algorithm used [1] is less robust close to the branching regions. This is especially true for our liver datasets where the blood contrast tends to weaken around branch points.…”

“…In fact, the curve evolution algorithm [5] produces accurate vascular segmentations by combining the modified curvature diffusion equation (MCDE) with a level-set based technique. On the other hand, Aylward et al [1] use a ridge traversal technique with width estimation to extract vascular centerline and estimated radius at each point along blood vessels. Both techniques have shown robustness to noise and high accuracy.…”

Accurate Quantification of Small-Diameter Tubular Structures in Isotropic CT Volume Data Based on Multiscale Line Filter Responses

“…When the effect of PSF is considered, the maximum response is inherently not obtainable for small-diameter structures which do not satisfy the condition 1 0.36 σ psf < D. Further, the output of this heuristics is discrete. In the proposed method, by least square fitting of multiscale responses to the theoretical multiscale response curve, accurate, continuous estimates are obtained even for small-diameter structures not satisfying 1 0.36 σ psf < D. For clinical application, a multiscale tracking method for tubular structures in 3D data as described in [11] can be effectively combined with the proposed method. We are now developing a method for scale-space tracking of extracted axels to combine with the proposed method.…”

“…The proposed method is related to the heuristics that the scale at which the maximum normalized response is obtained is selected as the optimal scale of the tubular structures [9], [10], [11]. The optimal scale σ opt is regarded as the true radius, that is, σ opt = 0.5D, when cross-sectional intensity distributions are Gaussian [9], while the relation σ opt = 0.36D is observed when they have pill-box shapes (as shown in Fig.…”

Estimating 3D lumen centerlines of carotid arteries in free-hand acquisition ultrasound

“…The extraction of centerlines from tubular structures in other modalities, than free-hand ultrasound, has been addressed in different works [4][5][6][7][8][9]. Schaap et al [10] give an extensive survey of the literature and present an experimental comparison of algorithms to extract centerlines of coronary arteries.…”