Semi-supervised tumor detection in magnetic resonance spectroscopic images using discriminative random fields

Authors:
L. Görlitz;B. H. Menze;M.-A. Weber;B. M. Kelm;F. A. Hamprecht
Affiliations:
Corporate Research, Robert Bosch GmbH, Germany and Multidim. Image Processing, Interdisciplinary Center for Scientific Computing, IWR, University of Heidelberg;Multidim. Image Processing, Interdisciplinary Center for Scientific Computing, IWR, University of Heidelberg;Department of Radiology, German Cancer Research Center, Heidelberg;Multidim. Image Processing, Interdisciplinary Center for Scientific Computing, IWR, University of Heidelberg;Multidim. Image Processing, Interdisciplinary Center for Scientific Computing, IWR, University of Heidelberg
Venue:
Proceedings of the 29th DAGM conference on Pattern recognition
Year:
2007

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Abstract

Magnetic resonance spectral images provide information on metabolic processes and can thus be used for in vivo tumor diagnosis. However, each single spectrum has to be checked manually for tumorous changes by an expert, which is only possible for very few spectra in clinical routine. We propose a semi-supervised procedure which requires only very few labeled spectra as input and can hence adapt to patient and acquisition specific variations. The method employs a discriminative random field with highly flexible single-side and parameter-free pair potentials to model spatial correlation of spectra. Classification is performed according to the label set that minimizes the energy of this random field. An iterative procedure alternates a parameter update of the random field using a kernel density estimation with a classification by means of the GraphCut algorithm. The method is compared to a single spectrum approach on simulated and clinical data.