DocumentCode :
3315299
Title :
A variational approach for the segmentation of the left ventricle in MR cardiac images
Author :
Paragios, Nikos
Author_Institution :
Siemens Corp. Res. Inc., Princeton, NJ, USA
fYear :
2001
fDate :
2001
Firstpage :
153
Lastpage :
160
Abstract :
This paper proposes a new front propagation method to segment MR cardiac images. This framework is based on the geodesic active region model, refers to a coupled propagation of two curves (inner and outer cardiac contours) and integrates boundary and region-based segmentation modules. The boundary information is introduced to the objective function using the gradient vector flow framework while the region information using continuous probability density functions. The defined objective function is minimised using a gradient descent method and the obtained motion equations are implemented using a level set approach. A recently introduced numerical approximation scheme with fast convergence rate and stable behavior is used to implement the level set motion equations. Finally, according to the application the propagations of the level set contours are coupled using their relative distances. Encouraging experimental results are provided using real data
Keywords :
approximation theory; biomedical MRI; cardiology; differential geometry; edge detection; gradient methods; image segmentation; medical image processing; minimisation; numerical stability; probability; variational techniques; MR cardiac images; boundary-based segmentation; continuous probability density functions; convergence rate; coupled propagation; curve contours; front propagation method; geodesic active region model; gradient descent method; gradient vector flow framework; left ventricle; level set approach; motion equations; numerical approximation; objective function minimisation; region-based segmentation; relative distances; stable behavior; variational approach; Application software; Convergence of numerical methods; Educational institutions; Equations; Image segmentation; Level set; Noise shaping; Probability density function; Shape; Surface fitting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Variational and Level Set Methods in Computer Vision, 2001. Proceedings. IEEE Workshop on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-1278-X
Type :
conf
DOI :
10.1109/VLSM.2001.938894
Filename :
938894
Link To Document :
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