Visualization in Medicine: Theory, Algorithms, and Applications
Visualization in Medicine: Theory, Algorithms, and Applications
Visual Analytics: Scope and Challenges
Visual Data Mining
Visual cluster analysis of trajectory data with interactive Kohonen maps
Information Visualization
Visual analytics of time dependent 2D point clouds
Proceedings of the 2009 Computer Graphics International Conference
Mutual information aspects of scale space images
Pattern Recognition
Techniques for precision-based visual analysis of projected data
Information Visualization - Special issue on selected papers from visualization and data analysis 2010
3D active shape model segmentation with nonlinear shape priors
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Visualization of Parameter Space for Image Analysis
IEEE Transactions on Visualization and Computer Graphics
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
Expert Systems with Applications: An International Journal
Guided visualization of ultrasound image sequences
EG VCBM'10 Proceedings of the 2nd Eurographics conference on Visual Computing for Biology and Medicine
A visual analytics framework for cluster analysis of DNA microarray data
Expert Systems with Applications: An International Journal
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
Expert Systems with Applications: An International Journal
MobileAR Browser - A generic architecture for rapid AR-multi-level development
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Segmentation of medical images is a prerequisite in clinical practice. Many segmentation algorithms use statistical shape models. Due to the lack of tools providing prior information on the data, standard models are frequently used. However, they do not necessarily describe the data in an optimal way. Model-based segmentation can be supported by Visual Analytics tools, which give the user a deeper insight into the correspondence between data and model result. Combining both approaches, better models for segmentation of organs in medical images are created. In this work, we identify the main tasks and problems in model-based image segmentation. As a proof of concept, we show that already small visual-interactive extensions can be very beneficial. Based on these results, we present research challenges for Visual Analytics in this area.