Analysis of breast MRI data based on (topographic) independent and tree-dependent component analysis

Abstract

In recent years, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become a powerful complement to X-ray based mammography in breast cancer diagnosis and monitoring. In DCE-MRI the time related development of the signal intensity after the administration of contrast agent can provide valuable information about tissue characteristics at pixel level. The integration of this information constitutes an important step in the analysis of DCE-MRI data. In this contribution we investigate the applicability of three different approaches from the field of independent component analysis (ICA) for feature extraction and image fusion in the context of DCE-MRI data. Next to FastICA, Tree-Dependent Component Analysis and Topographic ICA are applied to twelve clinical cases from breast cancer research with a histopathologically confirmed diagnosis. The outcome of all algorithms is quantitatively evaluated by means of Receiver Operating Characteristics (ROC) statistics. Additionally, the estimated 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 detection and subclassification and are rather robust with respect to their parameterization. However, with respect to ROC analysis Tree-Dependent Component Analysis tends to outperform all other algorithms as well as with regarding to the consistency of the results.