Geometric approaches have been very effective in quantifying and characterizing complex anatomical shape differences and changes in biomedical images. In image segmentation, various topological approaches such as level sets, graph cuts and fuzzy connectedness have been effective. However, it's very difficult to separate topology from geometry in images. Often the combinations of geometric and topological approaches are more effective in quantifying complex images. For instance, topological constraints are enforced to have consistent shape preserving image deformation. Theoretically, the Gauss-Bonnet theorem connects geometry and topology through a single mathematical equation. Recently, topological data analysis (TDA) has been popular in revealing topological features that are persistent over multiple scales. TDA often employs geometric methods in quantifying topological changes. The main aim of this workshop is to increase the awareness of the interaction between geometrical and topological approaches to the biomedical imaging community. The program will include invited talks, as well as regular oral and poster sessions with contributed research papers. The best paper and poster awards will be given.
- Joseph Reinhardt
Professor and Chair of Biomedical Engineering
University of Iowa
- Moo K. Chung
Associate Professor of Biostatistics and Medical Informatics
University of Wisconsin-Madison