Unsupervised tissue type segmentation of 3D dual-echo MR head data
Image and Vision Computing - Special issue: information processing in medical imaging 1991
Proceedings of the conference on Visualization '01
The Transfer Function Bake-Off
IEEE Computer Graphics and Applications
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
High-Quality Two-Level Volume Rendering of Segmented Data Sets on Consumer Graphics Hardware
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
A Novel Interface for Higher-Dimensional Classification of Volume Data
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Statistical modeling and conceptualization of visual patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A cluster-space visual interface for arbitrary dimensional classification of volume data
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
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This paper introduces a machine learning approach into the process of direct volume rendering of biomedical high-resolution 3D images. More concretely, it proposes a learning pipeline process that generates the classification function within the optical property function used for rendering. Briefly, this pipeline starts with a data acquisition and selection task, it is followed by a feature extraction process, to be ended with sequence of supervised learning steps. Learning comprises Gentle Boost and CRF (Conditional Random Fields) classifiers. The process is evaluated in terms of accuracy and overlap metrics so that we can measure how performance increases along the whole pipeline process. Empirical results confirm that, even though the classification of high-resolution computerized tomography volume data poses a challenging problem for single-run classifiers, it can be significantly improved by subsequent learning steps and refinements.