3-D quantification and visualization of vascular structures from confocal microscopic images using skeletonization and voxel-coding.
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.
Soltanian-Zadeh
Hamid
H
Image Analysis Laboratory, Department of Radiology, Henry Ford Health System, Detroit, MI 48202, USA. hamids@rad.hfh.edu
Shahrokni
Ali
A
Khalighi
Mohammad-Mehdi
MM
Zhang
Zheng G
ZG
Zoroofi
Reza A
RA
Maddah
Mahnaz
M
Chopp
Michael
M
D000818:Animals; D001808:Blood Vessels / Q000033:anatomy & histology*; D001921:Brain / Q000098:blood supply; D021621:Imaging, Three-Dimensional; D018613:Microscopy, Confocal / Q000379:methods*; D051381:Rats