Abstract
This paper analyzes how to introduce machine learning algorithms into the process of direct volume rendering. A conceptual framework for the optical property function elicitation process is proposed and particularized for the use of attribute-value classifiers. The process is evaluated in terms of accuracy and speed using four different off-the-shelf classifiers (J48, Naïve Bayes, Simple Logistic and ECOC-Adaboost). The empirical results confirm the classification of biomedical datasets as a tough problem where an opportunity for further research emerges.
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Cerquides, J. et al. (2006). Classification Algorithms for Biomedical Volume Datasets. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_16
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DOI: https://doi.org/10.1007/11881216_16
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