Abstract

Image-based bio-markers have become powerful diagnostic tools due to the rapid and amazing development of medical hardware. In such a context, efficient processing and understanding of the corresponding images has gained significant attention over the past decade. The task to be addressed is extremely challenging due to: (i) curse of non-linearity (images and desired bio-markers exhibit a non-linear relationship), (ii) curse of dimensionality (number of degrees of freedom versus their inference), (iii) curse of non-convexity (designed objective functions present numerous local minima) and (iv) curse of modularity (variability of organs, imaging modalities). In this talk, we will provide some preliminary answers to the aforementioned challenges by exploiting through graphical models and discrete optimization algorithms. Furthermore, concrete examples will be presented towards addressing fundamental problems in biomedical perception like knowledge-based segmentation and deformable image fusion.