Abstract
Features are extracted from PET images employing exploratory matrix factorization techniques, here non-negative matrix factorization (NMF). Appropriate features are fed into classifiers such as support vector machine or random forest. An automatic classification is achieved with high classification rate and only few false negatives.
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Kodewitz, A., Keck, I.R., Tomé, A.M., Górriz, J.M., Lang, E.W. (2010). Exploratory Matrix Factorization for PET Image Analysis. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_56
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DOI: https://doi.org/10.1007/978-3-642-13769-3_56
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