Defining spatial context for focused image analysis

Most human commonsense problem solving is done within a context, which constrains the solution space, whether it involves perception and image interpretation or not. Yet most research in image analysis still assumes context is defined a-priori by the investigator and is external to the computational image analysis system. Where explicit focus of attention and spatial contexts are used, as in active vision systems, these are problem-specific. We report on a new approach to image analysis, which includes a general framework for defining spatial context in terms of reference objects and their spatial relationships to other objects in a scene. Image analysis problems are decomposed into a sequence of sub-problems corresponding to determining a sequence of spatial contexts based on the set of dynamically chosen reference objects. The experimental results with medical image analysis and interpretation have demonstrated that using reference objects to define spatial context is a very effective strategy for computer image analysis systems that can purposely focus the image analysis effort on the most promising part of the entire image, such that the target object can be quickly and accurately localized by eliminating most potential false positives.