Browsing by Author "Preim, B."

Now showing items 1-5 of 5

    • Generation and Visual Exploration of Medical Flow Data: Survey, Research Trends and Future Challenges 

      Oeltze‐Jafra, S.; Meuschke, M.; Neugebauer, M.; Saalfeld, S.; Lawonn, K.; Janiga, G.; Hege, H.‐C.; Zachow, S.; Preim, B. (© 2019 The Eurographics Association and John Wiley & Sons Ltd., 2019)
      Simulations and measurements of blood and airflow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis and treatment of diseases. This ...

    • Guidelines for Quantitative Evaluation of Medical Visualizations on the Example of 3D Aneurysm Surface Comparisons 

      Saalfeld, P.; Luz, M.; Berg, P.; Preim, B.; Saalfeld, S. (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018)
      Medical visualizations are highly adapted to a specific medical application scenario. Therefore, many researchers conduct qualitative evaluations with a low number of physicians or medical experts to assess the benefits ...

    • A Survey on Multimodal Medical Data Visualization 

      Lawonn, K.; Smit, N.N.; Bühler, K.; Preim, B. (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018)
      Multi‐modal data of the complex human anatomy contain a wealth of information. To visualize and explore such data, techniques for emphasizing important structures and controlling visibility are essential. Such fused overview ...

    • Virtual Inflation of the Cerebral Artery Wall for the Integrated Exploration of OCT and Histology Data 

      Glaßer, S.; Hoffmann, T.; Boese, A.; Voß, S.; Kalinski, T.; Skalej, M.; Preim, B. (© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017)
      Intravascular imaging provides new insights into the condition of vessel walls. This is crucial for cerebrovascular diseases including stroke and cerebral aneurysms, where it may present an important factor for indication ...

    • Visual Analysis of Missing Values in Longitudinal Cohort Study Data 

      Alemzadeh, S.; Niemann, U.; Ittermann, T.; Völzke, H.; Schneider, D.; Spiliopoulou, M.; Bühler, K.; Preim, B. (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020)
      Attrition or dropout is the most severe missingness problem in longitudinal cohort study data where some participants do not show up for follow‐up examinations. Dropouts result in biased data and cause the reduction of ...