Abstract
Magnetic Resonance Spectroscopy (MRS) provides the biochemical composition of a tissue under study. This information is useful for the in-vivo diagnosis of brain tumours. Prior knowledge of the relative position of the organic compound contributions in the MRS suggests the development of a probabilistic mixture model and its EM-based Maximum Likelihood Estimation for binned and truncated data. Experiments for characterizing and classifying Short Time Echo (STE) spectra from brain tumours are reported.
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Garcia-Gomez, J.M., Robles, M., Van Huffel, S., Juan-Císcar, A. (2007). Modelling of Magnetic Resonance Spectra Using Mixtures for Binned and Truncated Data. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_34
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DOI: https://doi.org/10.1007/978-3-540-72849-8_34
Publisher Name: Springer, Berlin, Heidelberg
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