Model observers have gained popularity as a surrogate approach for image quality assessment, they are often used for the optimization of the reconstruction algorithm. The most widespread model observer is the channelized Hotelling observer (CHO) that allows measuring the image quality by calculating the detectability index (or associated area under receiver operating characteristic curve). In this work we have chosen to explore different resampling methods used to estimate the CHO performance and uncertainty. In this paper, using data from the inter-laboratory comparison of the computation of CHO model observer study, we established a simulation framework to fully evaluate different resampling methods, namely, leave-one out and bootstrapping with replacement to estimate the CHO’s detectability index bias and uncertainty. For this particular study, we focus our experiments on datasets with a few data samples, 200 normal and 200 abnormal images.
Francesc Massanes, Alexandre Ba, François Bochud, and Jovan G. Brankov, "A resampling comparison of CHO's detectability index bias and uncertainty," Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 105770V (Presented at SPIE Medical Imaging: February 12, 2018; Published: 12 March 2018); https://doi.org/10.1117/12.2297689.
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