Classification of Spectroscopic Images in the DIROlab Environment
We present the magnetic resonance spectroscopy imaging (MRSI) analysis functionality of DIROlab, an integrated software platform for cancer diagnosis and therapy planning. Completely automated estimation of cancer probability from the spectral signature is achieved by state-of-theart statistical classification techniques; furthermore an easy-to-use interface for spectrum labeling, classifier retraining and evaluation and the benchmarking and comparison of several alternative algorithms is currently under development. The effectiveness of this approach is exemplarily demonstrated by detecting adenocarcinoma in 1.5 Tesla MRSI measurements of the prostate.
KeywordsMagnetic resonance spectroscopy imaging computer- assisted diagnostics statistical classification prostate adenocarcinoma DIROlab
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