Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Performance of Distribution Tracking through Background Mismatch
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images
International Journal of Computer Vision
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Automatic Recovery of the Left Ventricular Blood Pool in Cardiac Cine MR Images
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Comprehensive Segmentation of Cine Cardiac MR Images
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Left Ventricle Tracking Using Overlap Priors
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
A Statistical Overlap Prior for Variational Image Segmentation
International Journal of Computer Vision
Improving segmentation of the left ventricle using a two-component statistical model
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Automatic cardiac 4d segmentation using level sets
FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
Segmenting and tracking the left ventricle by learning the dynamics in cardiac images
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Active contours for tracking distributions
IEEE Transactions on Image Processing
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Regional heart motion abnormality detection via information measures and unscented kalman filtering
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Recovering endocardial walls from 3D TEE
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
A convex max-flow segmentation of LV using subject-specific distributions on cardiac MRI
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Global assessment of cardiac function using image statistics in MRI
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model
Computer Vision and Image Understanding
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We present a discrete kernel density matching energy for segmenting the left ventricle cavity in cardiac magnetic resonance sequences. The energy and its graph cut optimization based on an original first-order approximation of the Bhattacharyya measure have not been proposed previously, and yield competitive results in nearly real-time. The algorithm seeks a region within each frame by optimization of two priors, one geometric (distance-based) and the other photometric, each measuring a distribution similarity between the region and a model learned from the first frame. Based on global rather than pixelwise information, the proposed algorithm does not require complex training and optimization with respect to geometric transformations. Unlike related active contour methods, it does not compute iterative updates of computationally expensive kernel densities. Furthermore, the proposed first-order analysis can be used for other intractable energies and, therefore, can lead to segmentation algorithms which share the flexibility of active contours and computational advantages of graph cuts. Quantitative evaluations over 2280 images acquired from 20 subjects demonstrated that the results correlate well with independent manual segmentations by an expert.