Abstract
The aim of this study was to investigate if the ability to detect clinically relevant signals, within local area clinically relevant texture, is related to experience. A two alternative forced choice interleaved staircase experiment was conducted on 101 observers split into three groups; group 1 with diagnostic experience, group 2 with experience of imaging but not of making a diagnosis and group 3 with no experience of imaging. Thresholds of detection within synthesized, clinically representative textures were measured for a 15 mm simulated lesion within an MR T1 weighted brain texture and a 2.5 mm diameter simulated lesion embedded within X-ray trabecular bone texture. The results showed that there was a significant difference in threshold detectability between the groups for the brain texture at the 95% significance level but not for the bone texture. The experienced group did not demonstrate a correlation between their bone and brain results. However, the inexperienced group had a significant correlation between the bone and brain results. There was a significant correlation between increasing experience and detectability but this was dependent on the composition of the local area anatomical noise.
References
1 Wood BP. Visual expertise. Radiology 1999;211:1–3.
2 Davies I, Sowden P, Hammond S, Ansell J. Expertise in categorising mammograms: a perceptual or conceptual skill? In: Kundel HL, editor. Image perception; 1994 13-18 Feb. 1994; Newport Beach, CA: The International Society for Optical Engineering; 1994
3 Esserman L, Cowley H, Eberle C, Kirkpatrick A, Chang S, Berbaum K, et al. Improving the accuracy of mammography: volume and outcome relationships. J Natl Cancer Inst 2002;94:369–75.
4 Kinnunen J, Gothlin JH, Totterman S. Effect of training and experience on radiologic diagnostic performance in midfacial trauma. Acta Radiol 1988;29:83–7.
5 Rackow PL, Spitzer VM, Hendee WR. Detection of low-contrast signals. A comparison of observers with and without radiology training. Invest Radiol 1987;22:311–4.
6 Caelli T, Julesz B. On perceptual analyzers underlying visual texture discrimination. Part I. Biol Cybernetics 1978;28:167–75.
7 Revesz G, Kundel HL, Graber MA. The influence of structured noise on the detection of radiologic abnormalities. Invest Radiol 1974;9:479–86.
8 Samei E, Flynn MJ, Peterson E, Eyler WR. Subtle lung nodules: influence of local anatomic variations on detection. Radiology 2003;228:76–84.
9 Kundel HL, Nodine CF, Thickman D, Carmody D, Toto L. Nodule detection with and without a chest image. Invest Radiol 1985;20:94–9.
10 Ohara K, Doi K, Metz CE, Giger ML. Investigation of basic imaging properties in digital radiography. 13. Effect of simple structured noise on the detectability of simulated stenotic lesions. Med Phys 1989;16:14–21.
11 Håkansson M, Båth M, Börjesson S, Kheddache S, Flinck A, Ullman G, et al. Nodule detection in digital chest radiography: effect of nodule location. Radiat Protect Dosim 2005;114:92–6.
12 Båth M, Håkansson M, Börjesson S, Hoeschen C, Tischenko O, Kheddache S, et al. Nodule detection in digital chest radiography: effect of anatomical noise. Radiat Protect Dosim 2005;114:109–13.
13 Burgess AE, Jacobson FL, Judy PF. Human observer detection experiments with mammograms and power-law noise. Med Phys 2001;28:419–37.
14 Båth M, Håkansson M, Börjesson S, Kheddache S, Grahn A, Bochud FO, et al. Nodule detection in digital chest radiography: part of image background acting as pure noise. Radiat Protect Dosim 2005;114:102–8.
15 Burgess AE. Comparison of receiver operating characteristic and forced choice observer performance measurement methods. Med Phys 1995;22:643–55.
16 Metz CE. ROC methodology in radiologic imaging. Invest Radiol 1986;21:720–33.
17 Ginsburg AP, Cannon MW. Comparison of three methods for rapid determination of threshold contrast sensitivity. Invest Ophthalmol Vis Sci 1983;24:798–802.
18 McKee SP, Klein SA, Teller DY. Statistical properties of forced-choice psychometric functions: implications of probit analysis. Perception Psychophys 1985;37:286–98.
19 Higgins KE, Jaffe MJ, Coletta NJ, Caruso RC, de Monasterio FM. Spatial contrast sensitivity. Importance of controlling the patient's visibility criterion. Arch Ophthalmol 1984;102:1035–41.
20 Kalloniatis M, Luu C. Webvision: Psychophysics of vision. http://retina.umh.es/Webvision/Psych1.html [Accessed 7 July 2006]
21 Hahn L. Staircase 2AFC. http://retina.anatomy.upenn.edu/ ∼lance/Psycho/Methods/staircase_2afc.html [Accessed 7 July 2006]
22 Goodenough DJ, Rossmann K, Lusted LB. Radiographic applications of signal detection theory. Radiology 1972;105:199–200.
23 Sherrier RH, Johnson GA, Suddarth SA, Chiles C, Hulka C, Ravin CE. Digital synthesis of lung nodules. Invest Radiol 1985;20:933–7.
24 Meaney F, Raudkivi U, McIntyre WJ, Gallagher JH, Haaga JR, Havrilla TR, et al. Detection of low-contrast lesions in computed body tomography: an experimental study of simulated lesions. Radiology 1980;134:149–54.
25 Samei E, Flynn MJ, Eyler WR. Simulation of subtle lung nodules in projection chest radiography. Radiology 1997;202:117–24.
26 Firbank MJ, Coulthard A, Harrison RM, Williams ED. A computer generated model for assessment of lesion edge sharpness in breast MRI (poster 0207). Imaging, Oncology, Science (IOS), May 2000, Birmingham, UK. Br J Radiol 2000;73 (Suppl.)::64
27 Delp EJ, Kashyap RL, Mitchell OR. Image data compression using autoregressive time series models. Pattern Recognition 1979;11:313–23.
28 Efros AA, Leung TK. Texture synthesis by non-parametric sampling. In: IEEE International Conference on Computer Vision; 1999: IEEE; 1999
29 Bochud FO, Abbey CK, Eckstein MP. Statistical texture synthesis of mammographic images with clustered lumpy backgrounds. Optics Express 1999;4:33–43.
30 Rolland JP. Synthesizing anatomical images for image understanding. In: Beutel J, Kundel HL, van Metter RL, editors. Handbook of medical imaging. Volume 1. Physics and psychophysics. Washington: SPIE Press, 2001:683–720
31 Brettle DS, Berry E, Smith MA. Synthesis of texture from clinical images. Image and Vision Computing 2003;21:433–45.
32 Eckstein MP, Ahumada AJ, Jr, Watson AB. Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise. J Opt Soc Am A Opt Image Sci 1997;14:2406–19.
33 SMPTE. RP 133: Specifications for medical diagnostic imaging test pattern for television monitors and hard-copy recording cameras. New York: Society of Motion Pictures and Television Engineers, 1991
34 NEMA. Digital Imaging and Communications in Medicine (DICOM) Part 14: Grayscale display function. Virginia: National Electrical Manufacturers Association, 2003
35 Burgess AE, Wagner RF, Jennings RJ, Barlow HB. Efficiency of human visual signal discrimination. Science 1981;214:93–4.
36 Allen J. The intensity JND comes from Poisson neural noise: Implications for image coding. In: Rogowiz BE, Pappas TN, editors. Proceedings of SPIE Human Vision & Electronic Imaging V; 2000; San Jose, CA: SPIE, 2000
37 Altman DG. Practical statistics for medical research (9 edn) Boca Raton, FL: Chapman & Hall/CRC, 1999
38 Burgess AE, Colborne B. Visual signal detection. IV. Observer inconsistency. J Opt Soc Am A Opt Image Sci 1988:617–27
39 Ericsson KA, Charness N. Expert performance: Its structure and acquisition. Am Psychologist 1994;49:725–47.


