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- W1969653133 abstract "In recent years, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become a powerfulcomplement to X-ray based mammography in breast cancer diagnosis and monitoring. In DCE-MRI the timerelated development of the signal intensity after the administration of contrast agent can provide valuable informationabout tissue characteristics at pixel level. The integration of this information constitutes an importantstep in the analysis of DCE-MRI data.In this contribution we investigate the applicability of three different approaches from the field of independentcomponent analysis (ICA) for feature extraction and image fusion in the context of DCE-MRI data. Nextto FastICA, Tree-Dependent Component Analysis and Topographic ICA are applied to twelve clinical casesfrom breast cancer research with a histopathologically confirmed diagnosis. The outcome of all algorithms isquantitatively evaluated by means of Receiver Operating Characteristics (ROC) statistics. Additionally, theestimated components are discussed exemplarily and the corresponding data is visualized.The study suggests that all of the employed algorithms show some potential for the purposes of lesion detectionand subclassification and are rather robust with respect to their parameterization. However, with respect to ROCanalysis Tree-Dependent Component Analysis tends to outperform all other algorithms as well as with regardingto the consistency of the results." @default.
- W1969653133 created "2016-06-24" @default.
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- W1969653133 date "2007-04-09" @default.
- W1969653133 modified "2023-09-23" @default.
- W1969653133 title "Analysis of breast MRI data based on (topographic) independent and tree-dependent component analysis" @default.
- W1969653133 doi "https://doi.org/10.1117/12.720728" @default.
- W1969653133 hasPublicationYear "2007" @default.
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