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Saalbach, A., Schlossbauer,
T. and Barbu, A. “Visual
exploratory analysis of DCE-MRI data in
breast cancer based on novel nonlinear dimensional data reduction
techniques.” Proceedings of Society of
Photo-Optical Instrumentation Engineers Conference Series, SPIE 2009,
Vol. 7343, April 2009, Orlando, USA.
of multi-dimensional data sets becomes a critical and significant area
medical image processing. To analyze such high dimensional data, novel
nonlinear embedding approaches become increasingly important to show
dependencies among these data in a two- or three-dimensional space.
This paper investigates the potential of novel nonlinear dimensional
data reduction techniques and compares their results with proven
nonlinear techniques when applied to the differentiation of malignant
and benign lesions described by high-dimensional data sets arising from
dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Two
important visualization modalities in medical imaging are presented:
the mapping on a lower-dimensional data manifold and the image fusion.