M. López-Sánchez, J. Cerquides, D. Masip, and A. Puig (Spain)
Machine Learning, Biomedical 3D Images, Classification, CRF (Conditional Random Fields), GBP (Generalized Be lief Propagation).
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 learn ing pipeline process that generates the classification func tion within the optical property function used for rendering. Briefly, this pipeline starts with a data acquisition and se lection 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 mea sure 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.
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