Active shape models—their training and application
Computer Vision and Image Understanding
A hierarchical Markov modeling approach for the segmentation and tracking of deformable shapes
Graphical Models and Image Processing
Machine Learning
Statistics and Computing
Image Segmentation by Shape Particle Filtering
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Shape Particle Guided Tissue Classification
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Curve sampling and geometric conditional simulation
Curve sampling and geometric conditional simulation
Segmentation of Lumbar Vertebrae Using Part-Based Graphs and Active Appearance Models
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Evaluation of Cardiac Ultrasound Data by Bayesian Probability Maps
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Multi-object segmentation using shape particles
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Conditional point distribution models
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Automatic segmentation of vertebrae from radiographs: a sample-driven active shape model approach
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
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In this paper, we propose a sampling-based shape segmentation method that builds upon a global shape and a local appearance model. It is suited for challenging problems where there is high uncertainty about the correct solution due to a low signal-to-noise ratio, clutter, occlusions or an erroneous model. Our method suits for segmentation tasks where the number of objects is not known a priori, or where the object of interest is invisible and can only be inferred from other objects in the image. The method was inspired by shape particle filtering from de Bruijne and Nielsen, but shows substantial improvements to it. The principal contributions of this paper are as follows: (i) We introduce statistically motivated importance weights that lead to better performance and facilitate the application to new problems. (ii) We adapt the static sequential Monte Carlo (SMC) algorithm to the problem of image segmentation, where the algorithm proves to sample efficiently from high-dimensional static spaces. (iii) We evaluate the static SMC sampler on shapes on a medical problem of high relevance: the automated quantification of aortic calcifications on X-ray radiographs for the prognosis and diagnosis of cardiovascular disease and mortality. Our results suggest that the static SMC sampler on shapes is more generic, robust, and accurate than shape particle filtering, while being computationally equally costly.