1. ¹Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany
2. ²Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany

Correspondence: Axel Saalbach, Neuroinformatics Group, Faculty of Technology, Bielefeld University, PO Box 10 01 31, 33501 Bielefeld, Germany. Tel: +49 (0)521 106 6054; Fax: +49 (0)521 106 6011; E-mail: asaalbac@techfak.uni-bielefeld.de

Received 20 September 2004; Revised 11 February 2005; Accepted 16 February 2005; Published online 13 October 2005.

Abstract

The analysis of multivariate image data is a field of research that is becoming increasingly important in a broad range of applications from remote sensing to medical imaging. While traditional scientific visualization techniques are often not suitable for the analysis of this kind of data, methods of image fusion have evolved as a promising approach for synergistic data integration. In this paper, a new approach for the analysis of multivariate image data by means of image fusion is presented, which employs topographic mapping techniques based on non-Euclidean geometry. The hyperbolic self-organizing map (HSOM) facilitates the exploration of high-dimensional data and provides an interface in the tradition of distortion-oriented presentation techniques. For the analysis of hidden patterns and spatial relationships, the HSOM gives rise to an intuitive and efficient framework for the dynamic visualization of multivariate image data by means of color. In an application, the hyperbolic data explorer (HyDE) is employed for the visualization of image data from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Using 12 image sequences from breast cancer research, the method is introduced by different visual representations of the data and is also quantitatively analyzed. The HSOM is compared to different standard classifiers and evaluated with respect to topology preservation.