Original paper| Volume 33, P95-105, January 2017

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

Published:December 28, 2016DOI:


      • 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.



      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.


      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.


      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.


      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.


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        • Mackenzie A.
        • Warren L.M.
        • Wallis M.G.
        • Given-Wilson R.M.
        • Cooke J.
        • Dance D.R.
        • et al.
        The relationship between cancer detection in mammography and image quality measurements.
        Phys Med. 2016; 32: 568-574
        • Bouwman R.W.
        • van Engen R.E.
        • Broeders M.J.M.
        • den Heeten G.J.
        • Dance D.R.
        • Young K.C.
        Can the non-pre-whitening model observer, including aspects of the human visual system, predict human observer performance in mammography?.
        Phys Med. 2016; 32: 1559-1569
        • Visser R.
        • Veldkamp W.J.H.
        • Beijerinck D.
        • Bun P.A.M.
        • Deurenberg J.J.M.
        • Imhof-Tas M.W.
        • et al.
        Increase in perceived case suspiciousness due to local contrast optimisation in digital screening mammography.
        Eur Radiol. 2012; 22: 908-914
        • Warren L.M.
        • Given-Wilson R.M.
        • Wallis M.G.
        • Cooke J.
        • Halling-Brown M.D.
        • Mackenzie A.
        • et al.
        The effect of image processing on the detection of cancers in digital mammography.
        Am J Roentgenol. 2014; 203: 387-393
        • Park S.
        • Badano A.
        • Gallas B.D.
        • Myers K.J.
        Incorporating human contrast sensitivity in model observers for detection tasks.
        IEEE Trans Med Imaging. 2009; 28: 339-347
        • Abbey C.K.
        • Eckstein M.P.
        Oberver models as a surrogate to perception experiments.
        in: Samei E. Krupinski E. The handbook of medical image perception and techniques. Cambridge University Press, 2014: 240-250
        • Goffi M.
        • Veldkamp W.J.H.
        • Engen R.E.
        • Bouwman R.W.
        Evaluation of six channelized Hotelling observers in combination with a contrast sensitivity function to predict human observer performance.
        Medical Imaging, SPIE, 2015 (p. 94160Z-Z-9)
        • Avanaki A.R.
        • Espig K.S.
        • Xthona A.
        • Kimpe T.R.
        • Bakic P.R.
        • Maidment A.D.
        It is hard to see a needle in a haystack: modeling contrast masking effect in a numerical observer.
        Springer, Breast Imaging2014: 723-730
        • Avanaki A.R.N.
        • Espig K.S.
        • Maidment A.D.A.
        • Marchessoux C.
        • Bakic P.R.
        • Kimpe T.R.L.
        Development and evaluation of a 3D model observer with nonlinear spatiotemporal contrast sensitivity.
        in: Mello-Thoms C.R. Kupinski M.A. Medical imaging. SPIE, 2014 (p. 90370X-X-12)
        • Castella C.
        • Kinkel K.
        • Descombes F.
        • Eckstein M.P.
        • Sottas P.E.
        • Verdun F.R.
        • et al.
        Mammographic texture synthesis: second-generation clustered lumpy backgrounds using a genetic algorithm.
        Opt Express. 2008; 16: 7595-7607
        • Zhang Y.
        • Pham B.T.
        • Eckstein M.P.
        Evaluation of internal noise methods for Hotelling observer models.
        Med Phys. 2007; 34: 3312-3322
        • Park S.
        • Zhang G.
        • Myers K.
        Comparison of channel methods and observer models for the task-based assessment of multi-projection imaging in the presence of structured anatomical noise.
        IEEE Trans Med Imaging. 2016;
        • Marcelja S.
        Mathematical description of the responses of simple cortical cells.
        J Opt Soc Am A. 1980; 70: 1297-1300
        • Wunderlich A.
        • Noo F.
        Image covariance and lesion detectability in direct fan-beam x-ray computed tomography.
        Phys Med Biol. 2008; 53: 2471-2493
        • Watson A.B.
        Detection and recognition of simple spatial forms.
        in: Braddick O.J. Sleigh A.C. Physical and biological processing of images: proceedings of an international symposium organised by the Rank Prize Funds, London, England, 27–29 September, 1982. Berlin, Heidelberg, Springer, Berlin Heidelberg1983: 100-114
        • Abbey C.K.
        • Barrett H.H.
        Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability.
        J Opt Soc Am A. 2001; 18: 473-488
      1. Daly SJ. Visible differences predictor: an algorithm for the assessment of image fidelity. Medical Imaging, SPIE1992. p. 2–15.

        • Burgess A.E.
        • Li X.
        • Abbey C.K.
        Visual signal detectability with two noise components: anomalous masking effects.
        J Opt Soc Am A. 1997; 14: 2420-2442
        • Gallas B.D.
        • Barrett H.H.
        Validating the use of channels to estimate the ideal linear observer.
        J Opt Soc Am A. 2003; 20: 1725-1738
        • Ge D.
        • Zhang L.
        • Cavaro-Menard C.
        • Le Callet P.
        Numerical stability issues on channelized Hotelling observer under different background assumptions.
        J Opt Soc Am A. 2014; 31: 1112-1117
        • Witten J.M.
        • Park S.
        • Myers K.J.
        Partial least squares: a method to estimate efficient channels for the ideal observers.
        IEEE Trans Med Imaging. 2010; 29: 1050-1058
        • Reiser I.
        • Glick S.
        Tomosynthesis imaging.
        Taylor & Francis, 2014
        • Samei E.
        • Badano A.
        • Chakraborty D.
        • Compton K.
        • Cornelius C.
        • Corrigan K.
        • et al.
        Assessment of display performance for medical imaging systems: executive summary of AAPM TG18 report.
        Med Phys. 2005; 32: 1205-1225
        • McCann J.J.
        • Rizzi A.
        The art and science of HDR imaging.
        Wiley, 2011
      2. Avanaki ARN, Espig KS, Kimpe TRL, Maidment ADA. On anthropomorphic decision making in a model observer. In: Mello-Thoms CR, Kupinski MA, editors. Medical Imaging: SPIE; 2015. p. 941610–12.

        • Barten P.G.J.
        Contrast sensitivity of the human eye and its effect on image quality.
        SPIE Press book, 1999
        • Reiser I.
        • Nishikawa R.M.
        Identification of simulated microcalcifications in white noise and mammographic backgrounds.
        Med Phys. 2006; 33: 2905-2911
        • Gallas B.D.
        One-shot estimate of MRMC variance: AUC.
        Acad Radiol. 2006; 13: 353-362
      3. Barrett HH, Abbey CK, Gallas BD, Eckstein MP. Stabilized estimates of Hotelling-observer detection performance in patient-structured noise. Medical Imaging, SPIE1998. p. 27–43.

        • Gagne R.M.
        • Gallas B.D.
        • Myers K.J.
        Toward objective and quantitative evaluation of imaging systems using images of phantoms.
        Med Phys. 2006; 33: 83-95
        • Burgess A.E.
        Comparison of receiver operating characteristic and forced choice observer performance measurement methods.
        Med Phys. 1995; 22: 643-655
      4. Bouwman RW, van Engen RE, Broeders MJM, Den Heeten GJ, Young KC, Dance DR, et al. A framework to design experiments to link human and model observers for image quality analysis MIPS XVI. Ghent 2015.

      5. Kramer M. R2 statistics for mixed models. Proceedings of the Conference on Applied Statistics in Agriculture 2005. p. 148–60.

        • Castella C.
        • Abbey C.K.
        • Eckstein M.P.
        • Verdun F.R.
        • Kinkel K.
        • Bochud F.O.
        Human linear template with mammographic backgrounds estimated with a genetic algorithm.
        J Opt Soc Am A. 2007; 24: B1-12
        • Eckstein M.P.
        • Ahumada A.J.
        • Watson A.B.
        Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise.
        J Opt Soc Am A. 1997; 14: 2406-2419