Journal of Mathematical Imaging and Vision
Zoom-invariant vision of figural shape: the mathematics of cores
Computer Vision and Image Understanding
Zoom-invariant vision of figural shape: effects on cores of image disturbances
Computer Vision and Image Understanding
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Analysis of the Parameter Space of a Metric for Registering 3D Vascular Images
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Volume Rendering of Segmented Tubular Objects
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Registration and Analysis of Vascular Images
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
Preprocessing based statistical segmentation of MRA dataset
VRCAI '04 Proceedings of the 2004 ACM SIGGRAPH international conference on Virtual Reality continuum and its applications in industry
IEEE Transactions on Visualization and Computer Graphics
Comprehensive Cardiovascular Image Analysis Using MR and CT at Siemens Corporate Research
International Journal of Computer Vision
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Automatic localization and quantification of intracranial aneurysms
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Spinal crawlers: deformable organisms for spinal cord segmentation and analysis
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Line filtering for surgical tool localization in 3D ultrasound images
Computers in Biology and Medicine
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This paper introduces a technique for the automated description of tubular objects in 3D medical images. The goal of automated 3D object description is to extract a representation which consistently details the location, size, and structure of objects in 3D images using minimal user interaction. Such a representation provides a means which objects can be classified, quantifiably evaluated, and registered. It also serves as a region of interest specification for visualization processes.The technique presented in this paper is suited for generating representations of 3D objects with nearly circular cross sections which have, possibly as a result of a global operation (e.g., blurring), intensity extrema near their centers. Such tubular objects commonly occur within human anatomy (e.g., vessels and selected bones). The medial axis of each of these objects is well approximated its intensity ridge. The scales of the local maxima in medialness at all points along the ridge can be mapped to local width estimates. Together these measures capture the location, size, and structure of tubular objects.This paper covers the mathematical basis, the implementation issues, and the application of this technique to the extraction of vessels from 3D magnetic resonance angiographic images and bones from 3D X-ray computed tomographic images.