A focus+context technique based on hyperbolic geometry for visualizing large hierarchies
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A unifying objective function for topographic mappings
Neural Computation
Dynamic color mapping of bivariate qualitative data
VIS '97 Proceedings of the 8th conference on Visualization '97
Handbook of medical imaging
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Self-Organizing Maps
Cluster Analysis of Biomedical Image Time-Series
International Journal of Computer Vision
IEEE Computer Graphics and Applications
Exploring Large Graphs in 3D Hyperbolic Space
IEEE Computer Graphics and Applications
30 Years of Multidimensional Multivariate Visualization
Scientific Visualization, Overviews, Methodologies, and Techniques
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
An architecture for rule-based visualization
VIS '93 Proceedings of the 4th conference on Visualization '93
Information Visualization: Perception for Design
Information Visualization: Perception for Design
Information Systems - Knowledge discovery and data mining (KDD 2002)
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Feature extraction techniques for exploratory visualization of vector-valued imagery
IEEE Transactions on Image Processing
Growing a hypercubical output space in a self-organizing feature map
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
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.