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International Journal of Biomedical Imaging
Volume 2011, Article ID 920401, 16 pages
http://dx.doi.org/10.1155/2011/920401
Research Article

Automation of Hessian-Based Tubularity Measure Response Function in 3D Biomedical Images

Physiological Imaging Research Laboratory, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA

Received 4 August 2010; Revised 12 October 2010; Accepted 10 December 2010

Academic Editor: Yangbo Ye

Copyright © 2011 Oleksandr P. Dzyubak and Erik L. Ritman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The blood vessels and nerve trees consist of tubular objects interconnected into a complex tree- or web-like structure that has a range of structural scale 5 m diameter capillaries to 3 cm aorta. This large-scale range presents two major problems; one is just making the measurements, and the other is the exponential increase of component numbers with decreasing scale. With the remarkable increase in the volume imaged by, and resolution of, modern day 3D imagers, it is almost impossible to make manual tracking of the complex multiscale parameters from those large image data sets. In addition, the manual tracking is quite subjective and unreliable. We propose a solution for automation of an adaptive nonsupervised system for tracking tubular objects based on multiscale framework and use of Hessian-based object shape detector incorporating National Library of Medicine Insight Segmentation and Registration Toolkit (ITK) image processing libraries.