Active shape models—their training and application
Computer Vision and Image Understanding
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
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
A static SMC sampler on shapes for the automated segmentation of aortic calcifications
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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In this paper, we propose an efficient method for drawing shape samples using a point distribution model (PDM) that is conditioned on given points. This technique is suited for sample-based segmentation methods that rely on a PDM, e.g. [6], [2] and [3]. It enables these algorithms to effectively constrain the solution space by considering a small number of user inputs -- often one or two landmarks are sufficient. The algorithm is easy to implement, highly efficient and usually converges in less than 10 iterations. We demonstrate how conditional PDMs based on a single user-specified vertebra landmark significantly improve the aorta and vertebrae segmentation on standard lateral radiographs. This is an important step towards a fast and cheap quantification of calcifications on X-ray radiographs for the prognosis and diagnosis of cardiovascular disease (CVD) and mortality