Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Automatic Hip Bone Segmentation Using Non-Rigid Registration
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Semi-supervised statistical region refinement for color image segmentation
Pattern Recognition
Objective outcome evaluation of breast surgery
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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Digital 3D models of patients' organs or tissues are often needed for surgical planning and outcome evaluation, or to select prostheses adapted to patients' anatomy. Tissue classification is one of the hardest problems in automatic model generation from raw data. The existing solutions do not give reliable estimates of the accuracy of the resulting model. We propose a simple generative model using Gaussian Mixture Models (GMMs) to describe the likelihood functions involved in the computation of posterior probabilities. Multiscale feature descriptors are used to exploit the surrounding context of each element to be classified. Supervised learning is carried out using datasets manually annotated by expert radiologists. 3D models are generated from the binary volumetric models, obtained by labelling cortical bone pixels according to maximal likelihoods.