Statistical Support for Uncertainty in Radiological Diagnosis

D. Teather; B. A. Teather; N. P. Jeffery; G. H. du Boulay; B. du Boulay; M. Sharples

doi:10.1055/s-0038-1634262

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2000; 39(01): 1-6
DOI: 10.1055/s-0038-1634262

Original Article

Schattauer GmbH

Statistical Support for Uncertainty in Radiological Diagnosis

Authors

D. Teather

¹Department of Medical Statistics, De Montfort University, Leicester
B. A. Teather

¹Department of Medical Statistics, De Montfort University, Leicester
N. P. Jeffery

¹Department of Medical Statistics, De Montfort University, Leicester
G. H. du Boulay

¹Department of Medical Statistics, De Montfort University, Leicester

²Institute of Neurology, London
B. du Boulay

³School of Cognitive and Computing Sciences, University of Sussex, UK
M. Sharples

⁴School of Electronic and Electrical Engineering, University of Birmingham, UK

Abstract

Abstract:

Radiological interpretation and diagnosis involves the comparison and classification of complex medical images and is typical of the categorisation tasks that have been the subject of observational studies in Cognitive Science. This paper considers the affinity between statistical modelling and theories of categorisation for naturally occurring categories. Statistical based measures of similarity and typicality with a probabilistic interpretation are derived. The utilisation of these measures in the support of diagnosis under uncertainty via interactive overview plots is described. The application of the methodology to magnetic resonance imaging of the head is considered. The methods detailed have application to other fields involving archiving and retrieving of image data.

Keywords:

Statistical Diagnosis - Categorisation - Typicality - Similarity - Medical Imaging

Full Text

References

REFERENCES
1 du Boulay GH, Woods AJ, Teather D, Teather BA, Wills KM, Plummer D. Towards an advisor for MRI. Neuroradiol 1988; 30: 245-51.
2 du Boulay GH, Field B, Teather BA, Teather D, Plummer D. The extraction of expert knowledge for MR image acquisition from the published literature. Riv di Neuroradiol 1992; 5: 473-82.
3 du Boulay GH, Teather BA, Teather D, Santosh C, Best J. Structured reporting of MRI of the head in HIV. Neuroradiol 1994; 37: 144.
4 du Boulay GH, Teather BA, Teather D, Jeffery NP, Higgott M, Plummer D. Discriminating multiple sclerosis from other diseases of similar presentation – can a formal description approach help?. Riv di Neuroradiol 1994; 7: 37-45.
5 Gill PW. Observer variation in clinical diagnosis. A computer aided assessment of its magnitude and importance in 552 patients with abdominal pain. Method Inform Med 1973; 12: 108-13.
6 Teather D, Teather BA, Wills KM. et al. Evaluation of computer advisor in the interpretation of CT images of the head. Neuroradiol 1988; 30: 511-17.
7 Mervis CB, Rosch E. Categorisation of Natural Objects. Ann Review Psychol 1981; 32: 89-115.
8 De Dombal FT, Leaper DJ, Staniland JR, McCann AP, Horrocks J. Computer aided diagnosis. BMJ 1972; 2: 9-12.
9 Teather D. Diagnosis – methods and analysis. Bull IMA 1984; 10: 37-41.
10 Greenacre MJ. Correspondence Analysis in Practice. New York: Academic Press; 1993
11 Greenacre MJ. Simca Version 2: Personal Computer Software for Correspondence Analysis. Users Manual; 1990
12 du Boulay GH, Teather BA, Teather D, Thomas DJ, Jeffery NP. Differentiation of MS from vascular disease utilising detailed MR image descriptions. Neuroradiol 1999; 41: S32.
13 Kendall M, Stuart A. The Advanced Theory of Statistics Volume 1: Distribution Theory. 4th edition Griffin & Co; 1977
14 Shepard RN. Towards a universal law of generalisation for psychological science. Science 1987; 237: 1317-23.
15 Nosofsky RM. Overall similarity and the identification of separable-dimension stimuli. A choice model analysis. Percept Psycho-phys 1985; 38: 415-32.
16 Nosofsky RM. Relationship between exemplar-similarity and likelihood models of classification. J Math Psychol 1990; 34: 393-418.
17 Ennis DM, Palen JJ, Mullen K. A multidimensional stochastic theory of similarity. J Math Psychol 1988; 32: 449-65.
18 Ashby FG, Perrin NA. Toward a unified theory of similarity and recognition. Psychol Rev 1988; 95: 124-50.
19 Krumhansl CL. Concerning the applicability of geometric models to similarity data: The interrelationship between similarity and spatial density. Psychol Rev 1978; 83: 445-63.
20 Kushniruk AW, Patel VL, Marley AA. Small worlds and medical expertise: implications for medical cognition and knowledge engineering. Int J Med Inform 1998; 49: 255-71.
21 Bishop CM, Tipping ME. A hierarchical latent variable model for data visualisation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998; 20: 281-93.
22 Jog N, Shneiderman B. Starfield Informed Visualisation with Interactive Smooth Zooming. Proc. IFIP 2.6 Visual Databases Systems, Lausanne, Switzerland 1995: 1-10.
23 Ahlberg C, Shneiderman B. Visual information seeking: Tight coupling of dynamic query filters with starfield displays. In: CHI’94. New York: ACM Press; 1994: 313-17.
24 Bertin J. Semiology of Graphics. University of Wisconsin Press; 1983
25 Fishkin K, Stone MC. Enhanced dynamic queries via movable filters. Denver: CHI’95 Human Computer Interaction; 1995: 415-20.
26 Keim DA, Kriegel H. ViSDB:Database exploration using multidimensional visualisation. In: IEEE Computer Graphics and Applications. 1994: 40-9.
27 Jeffery NP, Teather BA, Teather D, Sharples M, du Boulay B, du Boulay GH. Computer based radiological training and eduction. In: Blackledge J. (ed) Image Processing: Mathematical Methods and Applications. Oxford: University Press; 1997
28 Santini S, Jain R. The graphical specification of similarity queries. J Visual Lang Comp 1996; 7: 403-21.