TY - GEN
T1 - Geometrically deformable templates for shape-based segmentation and tracking in cardiac MR images
AU - Rueckert, Daniel
AU - Burger, Peter
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1997.
PY - 1997
Y1 - 1997
N2 - We present a new approach to shape-based segmentation and tracking of multiple, deformable anatomical structures in cardiac MR images. We propose to use an energy-minimizing geometrically deformable template (GDT) which can deform into similar shapes under the influence of image forces. The degree of deformation of the template from its equilibrium shape is measured by a penalty function associated with mapping between the two shapes. In 2D, this term corresponds to the bending energy of an idealized thin-plate of metal. By minimizing this term along with the image energy terms of the classic deformable model, the deformable template is attracted towards objects in the image whose shape is similar to its equilibrium shape. This framework allows for the simultaneous segmentation of multiple deformable objects using intra-as well as inter-shape information. The energy minimization problem of the deformable template is formulated in a Bayesian framework and solved using relaxation techniques: Simulated Annealing (SA), a stochastic relaxation technique is used for segmentation while Iterated Conditional Modes (ICM), a deterministic relaxation technique is used for tracking. We present results of the algorithm applied to the reconstruction of the left and right ventricle of the human heart in 4D MR images.
AB - We present a new approach to shape-based segmentation and tracking of multiple, deformable anatomical structures in cardiac MR images. We propose to use an energy-minimizing geometrically deformable template (GDT) which can deform into similar shapes under the influence of image forces. The degree of deformation of the template from its equilibrium shape is measured by a penalty function associated with mapping between the two shapes. In 2D, this term corresponds to the bending energy of an idealized thin-plate of metal. By minimizing this term along with the image energy terms of the classic deformable model, the deformable template is attracted towards objects in the image whose shape is similar to its equilibrium shape. This framework allows for the simultaneous segmentation of multiple deformable objects using intra-as well as inter-shape information. The energy minimization problem of the deformable template is formulated in a Bayesian framework and solved using relaxation techniques: Simulated Annealing (SA), a stochastic relaxation technique is used for segmentation while Iterated Conditional Modes (ICM), a deterministic relaxation technique is used for tracking. We present results of the algorithm applied to the reconstruction of the left and right ventricle of the human heart in 4D MR images.
UR - http://www.scopus.com/inward/record.url?scp=0005482702&partnerID=8YFLogxK
U2 - 10.1007/3-540-62909-2_74
DO - 10.1007/3-540-62909-2_74
M3 - Conference contribution
AN - SCOPUS:0005482702
SN - 3540629092
SN - 9783540629092
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 83
EP - 98
BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings
A2 - Hancock, Edwin R.
A2 - Pelillo, Marcello
PB - Springer Verlag
T2 - International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 1997
Y2 - 21 May 1997 through 23 May 1997
ER -