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

Fan Yin Tzeng, Eric B. Lum, Kwan-Liu Ma

Research output: Contribution to journalArticlepeer-review

122 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)273-283
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number3
StatePublished - May 1 2005


  • Classification
  • Graphics hardware
  • Machine learning
  • Transfer functions
  • User interface design
  • Visualization
  • Volume rendering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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