An intelligent system approach to higher-dimensional classification of volume data

In volume data visualization, the classification step is used to determine voxel visibility and is usually carried out through the interactive editing of a transfer function that defines a mapping between voxel value and color/opacity. This approach is limited by the difficulties in working effectiv...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2005-05, Vol.11 (3), p.273-284
Main Authors: Tzeng, F.-Y., Lum, E.B., Ma, K.-L.
Format: Article
Language:English
Subjects:
Acceleration
Algorithms
Artificial Intelligence
Classification
Cluster Analysis
Computer Graphics
Data visualization
Feedback
Graphics
graphics hardware
Hardware
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Index Terms- User interface design
Information Storage and Retrieval - methods
Intelligent systems
Machine learning
Mapping
Numerical Analysis, Computer-Assisted
Pattern Recognition, Automated - methods
Rendering
Rendering (computer graphics)
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Studies
Transfer functions
User-Computer Interface
Visibility
visualization
volume rendering
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Summary:In volume data visualization, the classification step is used to determine voxel visibility and is usually carried out through the interactive editing of a transfer function that defines a mapping between voxel value and color/opacity. This approach is limited by the difficulties in working effectively in the transfer function space beyond two dimensions. We present a new approach to the volume classification problem which couples machine learning and a painting metaphor to allow more sophisticated classification in an intuitive manner. The user works in the volume data space by directly painting on sample slices of the volume and the painted voxels are used in an iterative training process. The trained system can then classify the entire volume. Both classification and rendering can be hardware accelerated, providing immediate visual feedback as painting progresses. Such an intelligent system approach enables the user to perform classification in a much higher dimensional space without explicitly specifying the mapping for every dimension used. Furthermore, the trained system for one data set may be reused to classify other data sets with similar characteristics.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2005.38