3-D quantification and visualization of vascular structures from confocal microscopic images using skeletonization and voxel-coding

Authors:
Hamid Soltanian-Zadeh;Ali Shahrokni;Mohammad-Mehdi Khalighi;Zheng G. Zhang;Reza A. Zoroofi;Mahnaz Maddah;Michael Chopp
Affiliations:
Image Analysis Laboratory, Department of Radiology, Henry Ford Health System, Detroit, MI 48202, USA and Control and Intelligent Processing Center of Excellence, Department of Electronics and Comp ...;Control and Intelligent Processing Center of Excellence, Department of Electronics and Computer Engineering, University of Tehran, Tehran 14399, Iran and School of Cognitive Sciences, Institute fo ...;Control and Intelligent Processing Center of Excellence, Department of Electronics and Computer Engineering, University of Tehran, Tehran 14399, Iran and Department of Neurology, Henry Ford Health ...;Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA;Control and Intelligent Processing Center of Excellence, Department of Electronics and Computer Engineering, University of Tehran, Tehran 14399, Iran;Control and Intelligent Processing Center of Excellence, Department of Electronics and Computer Engineering, University of Tehran, Tehran 14399, Iran and School of Cognitive Sciences, Institute fo ...;Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA
Venue:
Computers in Biology and Medicine
Year:
2005

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Abstract

This paper presents an image processing approach for information extraction from three-dimensional (3-D) images of vasculature. It extracts quantitative information such as skeleton, length, diameter, and vessel-to-tissue ratio for different vessels as well as their branches. Furthermore, it generates 3-D visualization of vessels based on desired anatomical characteristics such as vessel diameter or 3-D connectivity. Steps of the proposed approach are: (1) pre-processing, (2) distance mappings, (3) branch labeling, (4) quantification, and (5) visualization. We have tested and evaluated the proposed algorithms using simulated images of multi-branch vessels and real confocal microscopic images of the vessels in rat brains. Experimental results illustrate performance of the methods and usefulness of the results for medical image analysis applications.