Active shape models—their training and application
Computer Vision and Image Understanding
Self-Organizing Maps
International Journal of Human-Computer Studies - Special issue: Interactive graphical communication
A SOM Based Cluster Visualization and Its Application for False Coloring
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
POLYMECO " A Polygonal Mesh Comparison Tool
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
Visualization in Medicine: Theory, Algorithms, and Applications
Visualization in Medicine: Theory, Algorithms, and Applications
Visually driven analysis of movement data by progressive clustering
Information Visualization
Visual cluster analysis of trajectory data with interactive Kohonen maps
Information Visualization
A Visual Analytics Approach to Understanding Spatiotemporal Hotspots
IEEE Transactions on Visualization and Computer Graphics
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
IEEE Transactions on Visualization and Computer Graphics
Spatial Generalization and Aggregation of Massive Movement Data
IEEE Transactions on Visualization and Computer Graphics
Image-based rendering of intersecting surfaces for dynamic comparative visualization
The Visual Computer: International Journal of Computer Graphics
Distance Visualization for Interactive 3D Implant Planning
IEEE Transactions on Visualization and Computer Graphics
Guided visualization of ultrasound image sequences
EG VCBM'10 Proceedings of the 2nd Eurographics conference on Visual Computing for Biology and Medicine
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Dynamic multi-view exploration of shape spaces
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Visualization of vessel movements
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
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The importance of medical image segmentation increases in fields like treatment planning or computer aided diagnosis. For high quality automatic segmentations, algorithms based on statistical shape models (SSMs) are often used. They segment the image in an iterative way. However, segmentation experts and other users can only asses the final segmentation results, as the segmentation is performed in a "black box manner". Users cannot get deeper knowledge on how the (possibly bad) output was produced. Moreover, they do not see whether the final output is the result of a stabilized process.We present a novel Visual Analytics method, which offers this desired deeper insight into the image segmentation. Our approach combines interactive visualization and automatic data analysis. It allows the expert to assess the quality development (convergence) of the model both on global (full organ) and local (organ areas, landmarks) level. Thereby, local patterns in time and space, e.g., non-converging parts of the organ during the segmentation, can be identified. The localization and specifications of such problems helps the experts creating segmentation algorithms to identify algorithm drawbacks and thus it may point out possible ways how to improve the algorithms systematically.We apply our approach on real-world data showing its usefulness for the analysis of the segmentation process with statistical shape models.