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19 March 2009 Visual exploratory analysis of DCE-MRI data in breast cancer based on novel nonlinear dimensional data reduction techniques
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Abstract
Visualization of multi-dimensional data sets becomes a critical and significant area in modern 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.
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Anke Meyer-Bäse, Sylvain Lespinats, Frank Steinbrücker, Axel Saalbach, Thomas Schlossbauer, and Adrian Barbu "Visual exploratory analysis of DCE-MRI data in breast cancer based on novel nonlinear dimensional data reduction techniques", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73431A (19 March 2009); https://doi.org/10.1117/12.818296
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