Medical image analysis using high-dimensional information-theoretic criteria

Information-theoretic measures provide powerful criteria for solving low-level problems in mono- and multimodal medical imaging based on statistical descriptions of visual features. Important instances include segmentation and tracking, where the discrepancy between object/background features is measured using divergences; and motion estimation and registration, where inter-frame similarity is assessed by comparing jointly observed feature distributions using informations.