Article reference:

M. Egmont-Petersen, A.F. Frangi, W.J. Niessen, P.C.W. Hogendoorn, J.L. Bloem, M.A. Viergever, J.H.C. Reiber. "Segmentation of bone tumor in MR perfusion images using neural networks and multiscale pharmacokinetic features," In: A. Sanfeliu et al., Proceedings of the 15 th International Congress on Pattern Recognition, IEEE Computer Society, Barcelona, Vol. 4, pp. 80-83, 2000.


The decrease in the volume of viable tumor is an indicator for the effect preoperative chemotherapy has on bone tumors. We develop an approach for segmenting dynamic perfusion MR-images into viable tumor, nonviable tumor and healthy tissue. Two cascaded feed-forward neural networks are trained to perform the pixel-based segmentation. As features, we use parameters obtained from a pharmacokinetic model of the tissue perfusion (parametric images). Additional multiscale features that incorporate contextual information are included. Experiments indicate that multiscale blurred versions of the parametric images together with a multiscale formulation of the local image entropy are the most discriminative features.

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