12 April 2002 Matching statistical object models to real images
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Abstract
We advocate a task-based approach to measuring and optimizing image quality; that is, optimize imaging systems based on the performance of a particular observer performing a specific task. This type of analysis can require numerous images and is, thus, infeasible with real patients. Researchers are forced to employ statistical models from which they can produce as many images as required. We have developed methods to accurately fit statistical models of continuous objects to real images. The fitted models can be used for hardware optimizations as well as image-processing optimizations. We have employed a continuous lumpy object model in this research and found that our method can accurately determine model parameters in simulation.
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Matthew A. Kupinski, Eric Clakrson, Harrison H. Barrett, "Matching statistical object models to real images", Proc. SPIE 4686, Medical Imaging 2002: Image Perception, Observer Performance, and Technology Assessment, (12 April 2002); doi: 10.1117/12.462690; https://doi.org/10.1117/12.462690
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