The channelized-Hotelling observer (CHO) was investigated as a surrogate of human observers in task-based image quality assessment. The CHO with difference-of-Gaussian (DoG) channels has shown... Show moreThe channelized-Hotelling observer (CHO) was investigated as a surrogate of human observers in task-based image quality assessment. The CHO with difference-of-Gaussian (DoG) channels has shown potential for the prediction of human detection performance in digital mammography (DM) images. However, the DoG channels employ parameters that describe the shape of each channel. The selection of these parameters influences the performance of the DoG CHO and needs further investigation. The detection performance of the DoG CHO was calculated and correlated with the detection performance of three humans who evaluated DM images in 2-alternative forced-choice experiments. A set of DM images of an anthropomorphic breast phantom with and without calcification-like signals was acquired at four different dose levels. For each dose level, 200 square regions-of-interest (ROIs) with and without signal were extracted. Signal detectability was assessed on ROI basis using the CHO with various DoG channel parameters and it was compared to that of the human observers. It was found that varying these DoG parameter values affects the correlation (r(2)) of the CHO with human observers for the detection task investigated. In conclusion, it appears that the the optimal DoG channel sets that maximize the prediction ability of the CHO might be dependent on the type of background and signal of ROIs investigated. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) Show less
PurposeTo study the feasibility of a channelized Hotelling observer (CHO) to predict human observer performance in detecting calcification-like signals in mammography images of an anthropomorphic... Show morePurposeTo study the feasibility of a channelized Hotelling observer (CHO) to predict human observer performance in detecting calcification-like signals in mammography images of an anthropomorphic breast phantom, as part of a quality control (QC) framework.MethodsA prototype anthropomorphic breast phantom with inserted gold disks of 0.25 mm diameter was imaged with two different digital mammography x-ray systems at four different dose levels. Regions of interest (ROIs) were extracted from the acquired processed and unprocessed images, signal-present and signal-absent. The ROIs were evaluated by a CHO using four different formulations of the difference of Gaussian (DoG) channel sets. Three human observers scored the ROIs in a two-alternative forced-choice experiment. We compared the human and the CHO performance on the simple task to detect calcification-like disks in ROIs with and without postprocessing. The proportion of correct responses of the human reader (PCH) and the CHO (PCCHO) was calculated and the correlation between the two was analyzed using a mixed-effect regression model. To address the signal location uncertainty, the impact of shifting the DoG channel sets in all directions up to two pixels was evaluated. Correlation results including the goodness of fit (r(2)) of PCH and PCCHO for all different parameters were evaluated.ResultsSubanalysis by system yielded strong correlations between PCH and PCCHO, with r(2) between PCH and PCCHO was found to be between 0.926 and 0.958 for the unshifted and between 0.759 and 0.938 for the shifted channel sets, respectively. However, the linear fit suggested a slight system dependence. PCCHO with shifted channel sets increased CHO performance but the correlation with humans was decreased. These correlations were not considerably affected by of the DoG channel set used.ConclusionsThere is potential for the CHO to be used in QC for the evaluation of detectability of calcification-like signals. The CHO can predict the PC of humans in images of calcification-like signals of two different systems. However, a global model to be used for all systems requires further investigation. Show less
Ba, A.; Abbey, C.K.; Baek, J.; Han, M.; Bouwman, R.W.; Balta, C.; ... ; Bochud, F.O. 2018
