Digital Image Processing: Optimal Spatial Filter For Maximization Of The Perceived Snr Based On A Statistical Decision Theory Model For The Human Observer

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Abstract
In this study, we developed an optimal filter which maximizes the perceived SNR based on a statistical decision theory model for a human observer. This filter serves as a prewhitening filter that compensates for both the image noise Wiener spectrum and the observer's visual system response, thus allowing the observer to perform matched-filtering in a white noise background during signal detection. However, we found that the filtered image has to be displayed with a strong contrast enhancement factor in order to reduce the effects of observer's internal noise and the display system noise. The use of a large windowing factor results in an image exceeding the dynamic range of a display system. Due to this limitation, it appears to be difficult to implement the optimal statistical filter (OSF) effectively in a practical digital radiographic imaging system. Therefore, we examined alternative filters by using series approximation of the OSF. The perceived SNR's of the filtered images predicted by the statistical decision theory model indicate that these filters in combination with a moderate windowing factor can improve the detectability of signals over that achieved by the windowing technique alone. We discuss the theoretical basis for the development of these new filters and the results of our calculations. Examples of simple test object images processed by the filters are shown. The potential usefulness and limitations of the various image processing methods in practical settings are discussed.© (1985) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

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