Computer Vision/Computer Graphics Collaboration Techniques

Abstract

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.