Biomimicking the brain for biomedical image analysis

The production of images in medical applications and life sciences research is overwhelming. Mathematical paradigms for quantitative image analysis can be inspired by apparent models of early stages of visual perception. We discuss models of cortical neurons as multi-scale derivative operators for shape analysis, segmentation and retrieval, and adaptive feedback loops to the LGN for edge preserving smoothing. Voltage sensitive dye measurements of cortical columns inspire to multi-orientation contextual filters. The resulting algorithms lead to robust feature extraction, enhancement of dim lines and tensor valued images, retrieval of sub-scenes, and the extraction of dense motion vector fields. Many illustrative examples will be presented.