Feature-aware Volume Rendering of Ultrasound Data

Master Thesis


In volume rendering, Transfer Functions (TFs) link data information to visual properties that reveal relevant information to the user and enable interactive data exploration. TF research has been very successful with regard to Computed Tomography applications, but with other modalities, such as Magnetic Resonance data or Ultrasound data they have been less successful. For ultrasound data in particular, it would be helpful to identify features, i.e., data characteristics, which can be employed in the design of new TFs that support feature- and context-aware volumetric rendering. In this project, we would like to investigate, develop and evaluate novel strategies for feature extraction in ultrasound data and for the design of new feature-aware TFs. The project is conducted together with Dr. Katja Bühler from the VRVis Research Center. Upon successful completion of the project, there is potential of remuneration. 


  • A strong background in image processing
  • Knowledge of Volume Rendering
  • Interest and Knowledge in Medical Visualization
  • Knowledge of English
  • Excellent programming skills
  • Creativity and enthusiasm


For more information please contact Eduard Gröller, Katja Bühler .