The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds

Linear model observers based on statistical decision theory have been used successfully to predict human visual detection of aperiodic signals in a variety of noisy backgrounds. However, some models have included nonlinearities such as a transducer or nonlinear decision rules to handle intrinsic unc...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2006-10, Vol.25 (10), p.1348-1362
Main Authors: Yani Zhang, Pham, B.T., Eckstein, M.P.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biomimetics - methods
Brain modeling
Compression algorithms
Computer Simulation
Decision theory
Humans
Image compression
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
model observer
Models, Biological
nonlinear component
Nonlinear Dynamics
Observer Variation
Pattern Recognition, Automated - methods
Pattern Recognition, Visual - physiology
Predictive models
Reproducibility of Results
Sensitivity and Specificity
Signal detection
Studies
Task Performance and Analysis
Transducers
Transform coding
Uncertainty
Visual system
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Summary:Linear model observers based on statistical decision theory have been used successfully to predict human visual detection of aperiodic signals in a variety of noisy backgrounds. However, some models have included nonlinearities such as a transducer or nonlinear decision rules to handle intrinsic uncertainty. In addition, masking models used to predict human visual detection of signals superimposed on one of two identical backgrounds (masks) usually include a number of nonlinear components in the channels that reflect properties of the firing of cells in the primary visual cortex (V1). The effect of these nonlinearities on the ability of linear model observers to predict human signal detection in real patient structured backgrounds is unknown. We evaluate the effect of including different nonlinear human visual system components into a linear channelized Hotelling observer (CHO) using a signal known exactly but variable (SKEV) task. In particular, we evaluate whether the rank order of two compression algorithms (JPEG versus JPEG 2000) and two compression encoder settings (JPEG 2000 default versus JPEG 2000 optimized) based on model observer signal detection performance in X-ray coronary angiograms is altered by inclusion of nonlinear components. The results show: 1) the simpler linear CHO model observer outperforms CHO model with the nonlinear components; 2) the rank order of model observer performance for the compression algorithms/parameters does not change when the nonlinear components are included. For the present task and images, the results suggest that the addition of the nonlinearities to a channelized Hotelling model may add complexity to the model observers without great impact on rank order evaluation of image processing and/or acquisition algorithms
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2006.880681