Model-based analysis of biomedical images

The development of methods to accurately and reproducibly recover useful quantitative information from biomedical images is often hampered by uncertainties in handling the data related to: image acquisition parameters, the variability of normal human anatomy and physiology, the presence of disease or other abnormal conditions, and a variety of other factors. This talk will review image analysis strategies that make use of models based on geometrical and physical/biomechanical information to help constrain the range of possible solutions in the presence of such uncertainty. The discussion will be focused by looking primarily at several problem areas in the realms of neuroimaging- based structure/function analysis and cardiac function analysis, with an emphasis on image segmentation and motion/deformation tracking. In addition, some newer work related to applying similar ideas to image analysis from microscopy data will also be presented. The presentation will include a description of the problem areas and visual examples of the image datasets being used, an overview of the mathematical techniques involved and a presentation of results obtained when analyzing actual patient/ specimen image data using these methods. Emphasis will be placed on how image-derived information and appropriate modeling can be used together to address the image analysis and processing problems noted above. Recent efforts in the area of sparse representation and dictionary learning of image information will be emphasized.