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