Resolving Power and Decision Theory*†

Harris, James L.

doi:10.1364/josa.54.000606

Cited by 130 publications

(47 citation statements)

References 2 publications

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“…The distinction is borne out in much of the image-quality literature concerning hardware design where optimization is based on ideal observer analysis [3], [4] or related concepts such as noise-equivalent quanta and detective quantum efficiency [5], [7], the Hotelling trace [6], [7], and Fourier crosstalk [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments

Abbey¹,

Eckstein

2002

IEEE Trans. Med. Imaging

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For performing simple detection and discrimination tasks in image noise, human observers are often modeled by a cross-correlation between the image and an observer template followed by the injection of the observer's internal noise. This paper is concerned with estimating this template using the two-alternative forced-choice (2AFC) experimental paradigm. The basic idea behind the estimation procedure is to average the noise fields of the images used in a 2AFC experiment with a weight that depends on whether the observer got the trial correct or incorrect. We describe a method that produces unbiased estimates of the observer template up to a constant of proportionality under the linear cross-correlation model. The method proposed here is different from some previous methods in the way it assigns weights to the noise fields and we show that the resulting errors in the estimated template are minimized. We also propose and validate a formula for approximating the error covariance associated with the template estimates.

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Section: Introductionmentioning

confidence: 99%

Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments

Abbey¹,

Eckstein

2002

IEEE Trans. Med. Imaging

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“…Examples treated in the literature include the Rayleigh criterion task, i.e. the task of determining whether an image is that of a single lesion (star) or a binary lesion (star) system (see Harris 1964 for some related mensuration tasks see Hanson 1983), the task of deciding whether an image is that of a square or a circle (Roetling et al 1968) and the determination of the stronger of two signals (Burgess et al 1981). All of the statements made above in connection with signal detection apply directly to the task of signal discrimination if the difference of the expected signals Δs = s 1 − s 2 is used as the cross correlating or test mask.…”

mentioning

confidence: 99%

Unified SNR analysis of medical imaging systems

1985

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The ideal observer signal to noise ratio (SNR) has been derived from statistical decision theory for all of the major medical imaging modalities. This SNR provides an absolute scale for image system performance assessment and leads to instrumentation design goals and constraints for imaging system optimisation since no observer can surpass the performance of the ideal observer. The dependence of detectable detail size on exposure or imaging time follows immediately from the analysis. A framework emerges for comparing data acquisition techniques, e.g. reconstruction from projections versus Fourier methods in NMR imaging, and time of flight positron emission tomography (TOFPET) versus conventional PET. The approach of studying the ideal observer is motivated by measurements on human observers which show that they can come close to the performance of the idea) observer, except when the image noise has negative correlations-as in images reconstructed from projections-where they suffer a small but significant penalty.

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“…Equati on (10) shows that the optical power spectrum is determined by measuring the diffraction pattern intensity distributi on . Exact measurement of this distri bution is a formidable task because of the large dynamic range, (5 to 6 orders of magnitude is a typical range) and for patterns such as that represented by Figure 2, very high resolution is required .…”

Section: Diffraction Pattern Samplingmentioning

confidence: 99%