Characterizing vascular connectivity from micro CT images

Authors:
Marcel Jackowski;Xenophon Papademetris;Lawrence W. Dobrucki;Albert J. Sinusas;Lawrence H. Staib
Affiliations:
Departments of Diagnostic Radiology;Departments of Diagnostic Radiology and Biomedical Engineering;Departments of Diagnostic Radiology and Medicine, Cardiology, Yale University, New Haven CT;Departments of Diagnostic Radiology and Medicine, Cardiology, Yale University, New Haven CT;Departments of Diagnostic Radiology and Biomedical Engineering
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2005

Citing 4
Cited 3

Model-based detection of tubular structures in 3D images

Computer Vision and Image Understanding
Muliscale Vessel Enhancement Filtering

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Statistical 3D Vessel Segmentation Using a Rician Distribution

MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
A Multi-scale Line Filter with Automatic Scale Selection Based on the Hessian Matrix for Medical Image Segmentation

SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision

A Statistical Approach for Detecting Tubular Structures in Myocardial Infarct Scars

FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
Airway Tree Extraction with Locally Optimal Paths

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Coronary vein tracking from MSCT using a minimum cost path approach

ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro

Quantified Score

Hi-index	0.00

Visualization

Abstract

X-ray microCT (computed tomography) has become a valuable tool in the analysis of vascular architecture in small animals. Because of its high resolution, a detailed assessment of blood vessel physiology and pathology is possible. Vascular measurement from noninvasive imaging is important for the study and quantification of vessel disease and can aid in diagnosis, as well as measure disease progression and response to therapy. The analysis of tracked vessel trajectories enables the derivation of vessel connectivity information, lengths between vessel junctions as well as level of ramification, contributing to a quantitative analysis of vessel architecture. In this paper, we introduce a new vessel tracking methodology based on wave propagation in oriented domains. Vessel orientation and vessel likelihood are estimated based on an eigenanalysis of gray-level Hessian matrices computed at multiple scales. An anisotropic wavefront then propagates through this vector field with a speed modulated by the maximum vesselness response at each location. Putative vessel trajectories can be found by tracing the characteristics of the propagation solution between different points. We present preliminary results from both synthetic and mouse microCT image data.