Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 21, Issue 2, Pages 61-75Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/42.993126
Keywords
blood vessels; brain; geometric modeling; Hessian matrices; liver; lung
Categories
Funding
- NCI NIH HHS [R01CA67812] Funding Source: Medline
- PHS HHS [P01A47982] Funding Source: Medline
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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.
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