DescriptionIn biomedical imaging efficient and accurate reconstruction and tracking methods play a key role. Particularly in cell biology and medical tomography innovative spatio-temporal imaging models using advanced regularization techniques are of strongly growing interest.
The main goal of this talk is to highlight novel nonlinear PDE constrained variational methods and efficient primal-dual convex optimization methods for joint reconstruction and flow quantification. In those 4D imaging inverse problems, Bregman distances, nonlinear and higher-order regularization and optimal transport are the key ingredients. At the end I will also present a novel achievement for computing the spectral response (ground states) of nonlinear eigenvalue problems in the context of Mumford-Shah type functional with the purpose of automatic multi-scale segmentation.
Applications will include 4D in-vivo tracking of cells migrating through blood vessels and automatic tumor cell quantification for a EU project called Cancer-ID.
|Period||14 Oct 2015|
|Held at||Centre for Analysis, Scientific computing and Applications (CASA), Netherlands|
|Degree of Recognition||National|