Biomedical Image Informatics

We are investigating and developing methods providing efficient access to information encoded in biomedical images.

Research Priorities

Imaging is one of the key data acquisition technologies in medicine and life sciences and an extremely fast evolving technology delivering increasingly detailed insights into structure and function of organisms. This rich source of information enables fundamental insights into biological processes, but also supports the ongoing efforts to realize personalized medicine. However, scientists and physicians are confronted with an ongoing explosion of imaging data that has to be processed, inspected, analyzed and correlated and the ability to efficiently get access to the core information encoded in this data decides on its final usefulness.

The Biomedical Image Informatics Group is an interdisciplinary team addressing the above challenge by providing novel visual computing solutions covering complete image based workflows in medicine and life science.

Recent solutions developed by the group cover high performance image analysis solutions for radiology, visualization solutions for intervention planning, radar image analysis for environmental studies and the development of complete IT infrastructures for management, analysis, visualization and data mining of large image data collections for neurosciences. The group is IT partner of the Correlated Multi-Modal Imaging Node Austria (CMI) that is providing access to high end imaging modalities and imaging services in Austria.

Competences

  • Biomedical and industrial image processing and analysis
  • Machine learning, deep learning
  • Biomedical visualization
  • Image data mining and spatial indexing
  • Image data and open research data management
  • Human computer interaction

Key Publications

S. Muenzing, A. Thum, K. Bühler, D. Merhof (2017)
Evaluation of multi-channel image registration in Microscopy system of Drosophila larvae

M. Wimmer, D. Major, A. Novikov, K. Bühler (2016)
Local Entropy-optimized Texture Models for Semi-Automatic Spine Labeling in Various MRI Protocols. Conference Paper and Poster. ISBI 2016 (April 13-16 Prague)

N. Swoboda, J. Moosburner, S. Bruckner, J. Y. Yu, B. J. Dickson, K. Bühler (2016)
Visualization and Quantification for Interactive Analysis of Neural Connectivity in Drosophila. In Computer Graphics Forum.

M. Schlachter, T. Fechter, M. Jurisic, T. Schimek-Jasch, O. Oehlke, S. Adebahr, W. Birkfellner, U. Nestle, K. Bühler (2016)
Visualization of Deformable Image Registration Quality using Local Image Dissimilarity. In IEEE Transactions on Medical Imaging.

Martin Trapp, Florian Schulze; Alexey A. Novikov; Laszlo Tirian; Barry J. Dickson; Katja Bühler
Adaptive and Background-Aware GAL4 Expression Enhancement of Co-registered Confocal Microscopy Images. Neuroinformatics, 2016

For more publications click here.

References

Healthcare: AGFA Healthcare, BrainCon, Aquilab, Medtronics, Medical University Vienna, Universitaetsklinikum Freiburg, Oncolopole Toulouse, Donau University Krems, Faculdade de Medicina da Universidade de Lisboa, Medical University of Graz

Life Sciences and Biology: Institute of Molecular Pathology Vienna, CNRS - Tefor.net, HHMI, Baxter, University Konstanz, Correlated Multi Modal Imaging Node Austria, coopNatura

Computer Science and HCI: TU Wien, Vienna University, RWTH Aachen, University Magdeburg, TU Delft

Current Projects

Former Projects