Observer models utilizing compressed textures

Matthew A. Kupinski; Jiahua Fan

doi:10.1117/12.2581363

15 February 2021 Observer models utilizing compressed textures

Matthew A. Kupinski, Jiahua Fan

Proceedings Volume 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment; 115990I (2021) https://doi.org/10.1117/12.2581363
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

We have previously presented a method for sorting textures based on whether they obscure a signal, and thus hinder the ability of an observer to perform a signal-detection task, or whether the presence of certain textures can be easily ignored by the observer, and thus do little to impede performance. This analysis has led to a surrogate figure of merit that was demonstrated to correlate with human-observer performance as measured by the channelized Hotelling observer. In this work, we generalize our previous results to include more tasks including estimation and combined detection/estimation tasks. We demonstrate the ability of this method to determine the textures present in a set of images that are the most detrimental to the specified task. We further devise alternative surrogate figures of merit can utilize this texture-compression method as a mechanism for generating channels for observer-performance computations.

Conference Presentation

Citation Download Citation

Matthew A. Kupinski and Jiahua Fan "Observer models utilizing compressed textures", Proc. SPIE 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, 115990I (15 February 2021); https://doi.org/10.1117/12.2581363

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