A clustering-based system to automate transfer function design for medical image visualization

  • Authors:

  • Binh P. Nguyen;Wei-Liang Tay;Chee-Kong Chui;Sim-Heng Ong

  • Affiliations:

  • National University of Singapore, Department of Electrical and Computer Engineering, Singapore, Singapore;National University of Singapore, Department of Electrical and Computer Engineering, Singapore, Singapore;National University of Singapore, Department of Mechanical Engineering, Singapore, Singapore;National University of Singapore, Department of Electrical and Computer Engineering, and Division of Bioengineering, Singapore, Singapore

  • Venue:
  • The Visual Computer: International Journal of Computer Graphics
  • Year:
  • 2012

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Abstract

Finding good transfer functions for rendering medical volumes is difficult, non-intuitive, and time-consuming. We introduce a clustering-based framework for the automatic generation of transfer functions for volumetric data. The system first applies mean shift clustering to oversegment the volume boundaries according to their low-high (LH) values and their spatial coordinates, and then uses hierarchical clustering to group similar voxels. A transfer function is then automatically generated for each cluster such that the number of occlusions is reduced. The framework also allows for semi-automatic operation, where the user can vary the hierarchical clustering results or the transfer functions generated. The system improves the efficiency and effectiveness of visualizing medical images and is suitable for medical imaging applications.