DocumentCode :
1115693
Title :
A Hybrid Eulerian–Lagrangian Approach for Thickness, Correspondence, and Gridding of Annular Tissues
Author :
Rocha, Kelvin R. ; Yezzi, Anthony J., Jr. ; Prince, Jerry L.
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA
Volume :
16
Issue :
3
fYear :
2007
fDate :
3/1/2007 12:00:00 AM
Firstpage :
636
Lastpage :
648
Abstract :
We present a novel approach to efficiently compute thickness, correspondence, and gridding of tissues between two simply connected boundaries. The solution of Laplace´s equation within the tissue region provides a harmonic function whose gradient flow determines the correspondence trajectories going from one boundary to the other. The proposed method uses and expands upon two recently introduced techniques in order to compute thickness and correspondences based on these trajectories. Pairs of partial differential equations are efficiently computed within an Eulerian framework and combined with a Lagrangian approach so that correspondences trajectories are partially constructed when necessary. Examples are presented in order to compare the performance of this method with those of the pure Lagrangian and pure Eulerian approaches. Results show that the proposed technique takes advantage of both the speed of the Eulerian approach and the accuracy of the Lagrangian approach
Keywords :
Laplace equations; biological tissues; medical image processing; Laplace equation; annular tissue gridding; correspondence trajectories; gradient flow; harmonic function; hybrid Eulerian-Lagrangian approach; partial differential equations; Alzheimer´s disease; Cardiac disease; Cerebral cortex; Computed tomography; Heart; Lagrangian functions; Laplace equations; Magnetic resonance imaging; Myocardium; Partial differential equations; Correspondence; correspondence trajectory; partial differential equations (PDEs); thickness; Algorithms; Artificial Intelligence; Heart; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Models, Cardiovascular; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2007.891072
Filename :
4099404
Link To Document :
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