Validating the use of channels to estimate the ideal linear observer.

Image quality can be objectively defined according to how well an observer can perform a task of practical interest given the image. We review a practical model observer for the signal-detection task. The ideal observer for this task is a function of the image probability distributions, which are multidimensional and complicated. This observer is often too difficult to derive or estimate. An alternative to the ideal observer is the ideal linear observer, which can still be unmanageable. Our alternative is the ideal linear observer constrained to a small set of channels: the channelized-Hotelling observer.

[1]  K. Hanson,et al.  Detectability in computed tomographic images. , 1979, Medical physics.

[2]  Kyle J. Myers,et al.  Assessment of lesion detectability of Monte Carlo modeling of digital radiography systems , 2002, SPIE Medical Imaging.

[3]  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.

[4]  H. Barlow.  The efficiency of detecting changes of density in random dot patterns , 1978, Vision Research.

[5]  J. V. Vleck,et al.  A Theoretical Comparison of the Visual, Aural, and Meter Reception of Pulsed Signals in the Presence of Noise , 1946 .

[6]  H. V. Trees.  Detection, Estimation, And Modulation Theory , 2001 .

[7]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[8]  Michael A. King,et al.  Evaluation of the effect of scatter correction on lesion detection in hepatic SPECT imaging , 1995 .

[9]  Charles E. Metz.  Fundamental ROC Analysis , 2000 .

[10]  Jannick P. Rolland,et al.  Linear discriminants and image quality , 1991, Image Vis. Comput..

[11]  Charles E. Metz,et al.  On The Multiplex Advantage Of Coded Source/Aperture Photon Imaging , 1981, Other Conferences.

[12]  Stephan L. Faris,et al.  Using a digital anatomical phantom to optimize an imaging system , 1999, Medical Imaging.

[13]  Abraham Wald,et al.  Statistical Decision Functions , 1951 .

[14]  Theodore G. Birdsall,et al.  Definitions of d′ and η as Psychophysical Measures , 1958 .

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

[16]  Eric Clarkson,et al.  Transformation of characteristic functionals through imaging systems. , 2002, Optics express.

[17]  Kyle J. Myers,et al.  Foundations of Image Science , 2003, J. Electronic Imaging.

[18]  Stanford Goldman,et al.  Information theory , 1953 .

[19]  John A. Swets,et al.  Evaluation of diagnostic systems : methods from signal detection theory , 1982 .

[20]  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.

[21]  Kyle J. Myers,et al.  Multivariate Gaussian Pattern Classification: Effects of Finite Sample Size and the Addition of Correlated of Noisy Features on Summary Measures of Goodness , 1993, IPMI.

[22]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[23]  Robin N. Strickland,et al.  Channelized detection filters for detecting tumors in nuclear medical images , 1997, Medical Imaging.

[24]  A E Burgess.  Visual signal detection with two-component noise: low-pass spectrum effects. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[25]  D. C. Barber,et al.  Medical Imaging-The Assessment of Image Quality , 1996 .

[26]  Craig K. Abbey,et al.  Nodule detection in two-component noise: toward patient structure , 1997, Medical Imaging.

[27]  Neil A. Macmillan,et al.  Detection Theory: A User's Guide , 1991 .

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

[29]  Keinosuke Fukunaga,et al.  Effects of Sample Size in Classifier Design , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Brandon D. Gallas.  Variance of the channelized-hotelling observer from a finite number of trainers and testers , 2003, SPIE Medical Imaging.

[31]  H H Barrett,et al.  Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[32]  Kyle J. Myers,et al.  Megalopinakophobia: its symptoms and cures , 2001, SPIE Medical Imaging.

[33]  D G Brown,et al.  Detection performance of the ideal decision function and its McLaurin expansion: signal position unknown. , 1995, The Journal of the Acoustical Society of America.

[34]  Craig K. Abbey,et al.  Stabilized estimates of Hotelling-observer detection performance in patient-structured noise , 1998, Medical Imaging.

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

[36]  J. Swets.  Signal detection and recognition by human observers : contemporary readings , 1964 .

[37]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[38]  R. F. Wagner,et al.  Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance. , 1995, Journal of the Optical Society of America. A, Optics, image science, and vision.

[39]  H H Barrett,et al.  Effect of random background inhomogeneity on observer detection performance. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[40]  D. O. North,et al.  An Analysis of the factors which determine signal/noise discrimination in pulsed-carrier systems , 1963 .

[41]  Robert Stanton,et al.  Radiological Imaging: The Theory of Image Formation, Detection, and Processing , 1983 .

[42]  H H Barrett,et al.  Objective assessment of image quality: effects of quantum noise and object variability. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[43]  Craig K. Abbey,et al.  Human vs model observers in anatomic backgrounds , 1998, Medical Imaging.

[44]  Craig K. Abbey,et al.  A Practical Guide to Model Observers for Visual Detection in Synthetic and Natural Noisy Images , 2000 .