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Can the channelized Hotelling observer including aspects of the human visual system predict human observer performance in mammography?

Published:December 28, 2016DOI:https://doi.org/10.1016/j.ejmp.2016.12.015

      Highlights

      • The CHO has been used to predict human observer performance in a detection task.
      • The correlation between human and model observer performance was assessed.
      • Aspects of the HVS were added to study the predictive value of the CHO.
      • The most useful formulation of the CHO was found to depend on the task.
      • The CHO has potential to assess image quality objectively in mammography.

      Abstract

      Purpose

      In mammography, images are processed prior to display. Model observers (MO) are candidates to objectively evaluate processed images if they can predict human observer performance for detail detection. The aim of this study was to investigate if the channelized Hotelling observer (CHO) can be configured to predict human observer performance in mammography like images.

      Methods

      The performance correlation between human observers and CHO has been evaluated using different channel-sets and by including aspects of the human visual system (HVS). The correlation was investigated for the detection of disk-shaped details in simulated white noise (WN) and clustered lumpy backgrounds (CLB) images, representing respectively quantum noise limited and mammography like images. The images were scored by the MO and five human observers in 2-alternative forced choice experiments.

      Results

      For WN images the most useful formulation of the CHO to predict human observer performance was obtained using three difference of Gaussian channels without adding HVS aspects (RLR2 = 0.62). For CLB images the most useful formulation was the partial least square channel-set without adding HVS aspects (RLR2 = 0.71). The correlation was affected by detail size and background.

      Conclusions

      This study has shown that the CHO can predict human observer performance. Due to object size and background dependency it is important that the range of object sizes and allowed variability in background are specified and validated carefully before the CHO can be implemented for objective image quality assessment.

      Keywords

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