Title :
Model-based automatic recognition of blood vessels from MR images and its 3D visualization
Author :
Huang, Qian ; Stockman, George C.
Author_Institution :
Almaden Res. Center, IBM Corp., San Jose, CA, USA
Abstract :
A model-based approach is used for recognizing arterial blood vessels from MRA volumetric data. The modeling includes (1) a generalized stochastic tube model characterizing the structural properties of the vessels, and (2) a bivariate Gaussian function, modeling the expected cross sectional blood flow. This integrated model renders the recognition problem as a parameter estimation problem which is subsequently solved in a hierarchical fashion. Due to the descriptive representation for objects, a new visualization scheme for blood vessels is proposed that allows the observation of the blood flow of each cross section along a recognized vessel. Some experimental results from both synthetic data and real MRA volumes are given. The visualization of interior blood flow patterns for some vessels are also shown
Keywords :
biomedical NMR; flow visualisation; haemodynamics; image recognition; image segmentation; medical image processing; parameter estimation; 3D visualization; MR images; MRA volumetric data; arterial blood vessel recognition; automatic seeding; bivariate Gaussian function; cross sectional blood flow; generalized stochastic tube model; global recognition; hierarchical solution; interior blood flow pattern visualization; model-based automatic recognition; parameter estimation problem; segmentation; synthetic data; Biomedical imaging; Blood flow; Blood vessels; Data visualization; Image recognition; Image segmentation; Object oriented modeling; Shape; Solid modeling; Stochastic processes;
Conference_Titel :
Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference
Conference_Location :
Austin, TX
Print_ISBN :
0-8186-6952-7
DOI :
10.1109/ICIP.1994.413800
