BIM251W09_project

BIM251W09_project - = BIM 251 Medical Image Analysis...

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============================================================================ BIM 251 Medical Image Analysis Winter 2009 ============================================================================ Project Report due March 16 This project investigates the effect of post-processing filter on human observer efficiency for detecting a Gaussian signal in medical images. In tomographic image (CT, PET, SPECT), people have found that there is an optimal filter parameter ( β in this experiment) that gives the best lesion detection performance. Here we study the effect of filter using human observer 2AFC experiments. This is a SKE-BKE (flat background) task. 1. Use the MATLAB code below to generate a Gaussian-shape signal (nonrandom) and white Gaussian noise with mean zero and unity variance. Calculate the ideal observer SNR. 2. The frequency response of the filter used in tomographic reconstruction can be approximated by 22 2 || e x p () ,( ) , Hv uv u ρβ ρ −= + = . Apply the filter to the images that you generate in part 1. Show that the ideal observer SNR is unaffected by the filter. Find the expression for the NPS of the output of the filter.
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BIM251W09_project - = BIM 251 Medical Image Analysis...

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