Objective assessment of image quality: effects of quantum noise and object variability.

A number of task-specific approaches to the assessment of image quality are treated. Both estimation and classification tasks are considered, but only linear estimators or classifiers are permitted. Performance on these tasks is limited by both quantum noise and object variability, and the effects of postprocessing or image-reconstruction algorithms are explicitly included. The results are expressed as signal-to-noise ratios (SNR's). The interrelationships among these SNR's are considered, and an SNR for a classification task is expressed as the SNR for a related estimation task times four factors. These factors show the effects of signal size and contrast, conspicuity of the signal, bias in the estimation task, and noise correlation. Ways of choosing and calculating appropriate SNR's for system evaluation and optimization are also discussed.

[1]  R. F. Wagner,et al.  Efficiency of human visual signal discrimination. , 1981, Science.

[2]  A. Rose.  The sensitivity performance of the human eye on an absolute scale. , 1948, Journal of the Optical Society of America.

[3]  H H Barrett,et al.  Hotelling trace criterion and its correlation with human-observer performance. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[4]  Kenneth M. Hanson,et al.  Optimization for Object Localization of the Constrained Algebraic Reconstruction Technique , 1989, Medical Imaging.

[5]  K. M. Hanson,et al.  POPART - Performance Optimized Algebraic Reconstruction Technique , 1988, Other Conferences.

[6]  H H Barrett,et al.  Addition of a channel mechanism to the ideal-observer model. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[7]  C. Metz.  ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[8]  Kenneth M. Hanson,et al.  Method of evaluating image-recovery algorithms based on task performance , 1990 .

[9]  A E Burgess,et al.  Visual signal detection. IV. Observer inconsistency. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[10]  L B Lusted,et al.  Signal detectability and medical decision-making. , 1971, Science.

[11]  H H Barrett,et al.  Linear estimation theory applied to the evaluation of a priori information and system optimization in coded-aperture imaging. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[12]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[13]  H. Barrett,et al.  Effect of noise correlation on detectability of disk signals in medical imaging. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[14]  R. F. Wagner,et al.  Aperture optimization for emission imaging: effect of a spatially varying background. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[15]  Harrison H. Barrett,et al.  Hotelling trace criterion as a figure of merit for the optimization of imaging systems , 1986 .

[16]  R. F. Wagner,et al.  Unified SNR analysis of medical imaging systems , 1985, Physics in medicine and biology.

[17]  H. Hotelling.  The Generalization of Student’s Ratio , 1931 .