Abstract
The Item Response Theory (IRT) was applied to image processing where the item's difficulty parameter was used to reconstruct a region of interest in an image contaminated with noise. Shannon's entropy was used for the dichotomization of the image pixels. To demonstrate the efficiency of the proposed method, it was used on simulated data that can be related to functional magnetic resonance imaging (fMRI). The results on simulated data showed that IRT achieved a high percentage of accuracy in identifying the region of interest in the image and that, on average, the estimation converges to the true region of interest, thus proving to be a promising method for the analysis of such data.
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