Active shape models—their training and application
Computer Vision and Image Understanding
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A Unified Framework for Atlas Matching Using Active Appearance Models
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Boundary Finding with Correspondence Using Statistical Shape Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive Graph Cut Based Segmentation with Shape Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Multilevel Banded Graph Cuts Method for Fast Image Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Comprehensive Cardiovascular Image Analysis Using MR and CT at Siemens Corporate Research
International Journal of Computer Vision
International Journal of Computer Vision
Journal of Cognitive Neuroscience
Semiautomatic segmentation with compact shape prior
Image and Vision Computing
Star Shape Prior for Graph-Cut Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Graph Cuts Segmentation with Geometric Shape Priors for Medical Images
SSIAI '08 Proceedings of the 2008 IEEE Southwest Symposium on Image Analysis and Interpretation
A shape prior constraint for implicit active contours
Pattern Recognition Letters
Statistical modeling and segmentation in cardiac MRI using a grid computing approach
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
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Segmenting the right ventricle (RV) in magnetic resonance (MR) images is required for cardiac function assessment. The segmentation of the RV is a difficult task due to low contrast with surrounding tissues and high shape variability. To overcome these problems, we introduce a segmentation method based on a statistical shape model obtained with a principal component analysis (PCA) on a set of representative shapes of the RV. Shapes are not represented by a set of points, but by distance maps to their contour, relaxing the need for a costly landmark detection and matching process. A shape model is thus obtained by computing a PCA on the shape variations. This prior is registered onto the image via a very simple user interaction and then incorporated into the well-known graph cut framework in order to guide the segmentation. Our semi-automatic segmentation method has been applied on 248 MR images of a publicly available dataset (from MICCAI'12 Right Ventricle Segmentation Challenge). We show that encouraging results can be obtained for this challenging application.