Optical EngineeringParadigm for shape-based image analysis
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Traditional image segmentation techniques typically divide an image into separate regions based on gray-scale characteristics. Most real-world image-segmentation problems, however, require some subsequent shape-based processing to yield acceptable results. Unfortunately, choosing an appropriate sequence of image-processing operators (a process) for this purpose can be a time-consuming, tedious procedure that requires considerable image-processing expertise. We describe a semiautomatic paradigm for selecting shape-based operations for an image-analysis process. Desired shape information for image regions is provided by the user in the form of easily specified cues. The cues are then automatically interpreted to select suitable imageprocessing operators and operator parameters; the operators can be morphological, topological, and image-manipulation functions. The paradigm, hence, enables easy prototyping of image-analysis processes for different problems. The user is not required to be an image-processing expert to apply this strategyâ€”he or she need only be able to specify the desired shape properties of the regions in the image. We demonstrate our approach for both 2-D and 3-D image analysis problems.