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Knowledge based interpretation of images: a biomedical perspective

Published online by Cambridge University Press:  07 July 2009

Nicholas Walker  and
John Fox
Nicholas Walker
Affiliation:
Imperial Cancer Research Fund Laboratories, 44 Lincoln's Inn Fields, London, WC2A 3PX, UK
John Fox
Affiliation:
Imperial Cancer Research Fund Laboratories, 44 Lincoln's Inn Fields, London, WC2A 3PX, UK
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Abstract

The traditions of image processing and knowledge engineering have developed separately. Work on AI vision systems lies between the two traditions but only recently has attention been given to combining practical imaging systems with methods for exploiting knowledge in interpreting the contents of an image. Five general approaches to combining knowledge based expert systems with imaging technologies are discussed. Particular attention is paid to the requirement for techniques which transform a pixel array into a symbolic form suitable for interpretation, and current obstacles to a general solution. Interpretation of biomedical images is particularly problematic because of statistical, structural and temporal variation in morphology of objects and structures. Some ways in which knowledge of shape, structure, and object classifications may contribute to this interpretation are discussed. The survey focuses on biomedical images but many of the issues are of general relevance to work in image understanding.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 2 , Issue 4 , December 1987 , pp. 249 - 264
Copyright
Copyright © Cambridge University Press 1987

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References

Allen, J, 1984. “Towards a general theory of action and time” Artificial Intelligence 23(2) pp. 123–154.CrossRefGoogle Scholar
Brooks, R, Greiner, R and Binford, T, 1979. “The ACRONYM model-based vision system” Proc IJCAI 6 Tokyo 1979pp. 105–113.Google Scholar
Brooks, R, 1981. “Symbolic reasoning among 3-D and 2-D images” Artificial Intelligence I 17 pp. 285–348.CrossRefGoogle Scholar
Buxton, H and Walker, N, 1987. “Query based visual analysis: spatio-temporal reasoning in computer vision' Proceedings of Alvey Vision Conference, London.CrossRefGoogle Scholar
Clancey, W J, 1986. “Heuristic classifcation” Artificial Intelligence 27 pp. 215–251.Google Scholar
Davis, R and Buchanan, B G, 1984. “Meta-level knowledge” In: Rule-based expert systems, Buchanan, B G and Shortliffe, E H, Reading, Massachusetts: Addison-Wesley Chapter 28.Google Scholar
Ellam, S V and Maisey, M N, 1986. “A knowledge based system to assist in medical image interpretation: design and evaluation methodology” In: Research and Development in Expert Systems III, Bramer, M, (Ed.), Cambridge: Cambridge University Press.Google Scholar
Fox, J, 1978. “Continuity, concealment and visual attention” In: Strategies of Information Processing, Underwood, G, (ed.), London: Academic Press.Google Scholar
Fox, J, 1984. “Formal and knowledge-based methods in decision technology” Acta Psychologica, 56 pp. 303–331.CrossRefGoogle Scholar
Fox, J, 1986. “Three arguments for extending the framework of probability” In: Uncertainty in artificial intelligence, Kanal, L N and Lemmer, J F, (Eds.), Amsterdam: North Holland.Google Scholar
Fox, J, Glowinski, A J and O'Neil, M, 1987. “The Oxford system of medicine: a prototype information system for primary care” In: Proceedings of European Conference on Artificial Intelligence in Medicine, Fox, J, Fieschi, M and Engelbrecht, R, (Eds.), Berlin: Springer-Verlag.CrossRefGoogle Scholar
Forbus, K D, 1984. “Qualitative Process Theory” AI 24 pp. 85–169.Google Scholar
Gonzalez, R C and Wintz, P, 1977. Digital Image Processing, London: Addison-Wesley.Google Scholar
Grimson, W E, 1987. “Recognition of object families using parametized models” Proceedings of First International Conference on Computer Vision, IEEE Computer Society Press, pp. 93–101.Google Scholar
Hanson, A R and Riseman, E M, (Eds.), 1978. Computer Vision Systems, New York: Academic Press.Google Scholar
Hayes, P J, 1983. “The second naive physics manifesto” Representations of common sense pp. 468–485.Google Scholar
Lowe, D G, 1987. “Three-dimensional object recognition from single two-dimensional images” Artificial Intelligence, 31(3) 255–396.CrossRefGoogle Scholar
Marr, D and Nishihara, H K, 1978. “Representation and recognition of the spatial organisation of three dimensional shapes” Proc Royal Soc London, B 200 pp. 269–294.CrossRefGoogle Scholar
Matsuyama, T, 1984. “Knowledge organisation and control structure in image understanding” IEEE Proc International Conference Pattern Recognition (1984) pp. 1118–1127.Google Scholar
Nazif, A and Levine, M, 1984. “Low level image segmentation: an expert system” IEEE PAMI-6 No. 5 pp. 555–577.CrossRefGoogle Scholar
O'Neil, M, Glowinski, A J and Fox, J, 1988. “Decision making in the Oxford system of medicine” Proc. Conference on Expert Systems and their Applications, Avignon, 1988.Google Scholar
Pentland, A P, 1986. “Parts: structured descriptions of shape” Proceedings of AAAI, Pittsburgh pp. 695–701.Google Scholar
Rawlings, C J, Taylor, W, Fox, J, Nyakairu, J, Sternberg, M J E, 1985. “Reasoning about protein topology using the logic programming language Prolog” Journal of Molecular Graphics 3(4) pp. 151–157.CrossRefGoogle Scholar
Viergever, M A and Todd-Pokropek, A, (Eds.), 1988. Mathematics and Computer Science in medical imaging, Berlin: Springer.CrossRefGoogle Scholar
Binford, T 1982. “Survey of model-based image analysis systems” Internationaljournal of Robotics Research, 1 pp. 18–64.CrossRefGoogle Scholar
Brady, M, 1982. “Computational approaches to image understanding” Computing Surveys 14 no 1 pp. 3–71.CrossRefGoogle Scholar
Chin, R T and Dyer, C R, 1986. “Model-based recognition in robot vision” Computing Surveys 18 no 1 pp. 67–108.CrossRefGoogle Scholar
Gonzalez, R C and Wintz, P, 1977. Digital Image Processing, London: Addison-Wesley.Google Scholar
Hanson, A R and Riseman, E M, (Eds.) 1978. Computer Vision Systems, New York: Academic Press.Google Scholar
Marr, D, 1976. Vision, New York: Freeman.Google ScholarPubMed
Viergever, M A and Todd-Pokropek, A, 1988. Mathematics and Computer Science in medical imaging, Berlin: Springer.CrossRefGoogle Scholar
Brooks, R, Greiner, R and Binford, T, 1979. “The ACRONYM model-based vision system” Proc IJCAI 6 Tokyo 1979 pp. 105–113.Google Scholar
Brooks, R, 1981. “Symbolic reasoning among 3-D and 2-D images” Artificial Intelligence I 17 pp. 285–348.CrossRefGoogle Scholar
Lowe, D G, 1987. “Three-dimensional object recognition from single two-dimensional images” Artificial Intelligence 31(3) pp. 255–396.CrossRefGoogle Scholar
Ohta, Y, 1980. “A region-oriented image-analysis system by computer” PhD thesis Kyoto University, Dept of Information Science.Google Scholar
Matsuyama, T, 1984. “Knowledge organisation and control structure in image understanding” IEEE Proc Int Conf Pattern Recognition (1984) pp. 1118–1127.Google Scholar
Matsuyama, T and Hwang, V, 1985. “SIGMA, a framework for image understanding—integration of bottom-up and top-down analysis” Proc IJCAI 1985 pp. 908–915.Google Scholar
McKeown, D, 1985. “Rule-based interpretation of aerial imagery” IEEE PAMI-7 No 5 pp. 570–585.CrossRefGoogle Scholar
Niemann, H, 1985. “A knowledge based system for analysis of gated blood pool studies” IEEE PAMI-7 No. 3 pp. 246–259.CrossRefGoogle Scholar
Levine, M, Noble, P and Youssef, Y, 1983. “A rule-based system for characterising blood cell motion” In: Image Sequence Processing and Dynamic Scene Analysis Huang, T S (Ed.) NATO ASI series 2 Berlin: Springer Verlag pp. 663–709.CrossRefGoogle Scholar
Canny, J, 1983. “A variational approach to edge detection” Proceedings of National Conference on Artificial Intelligence AAAI-83.Google Scholar
Marr, D and Mildreth, E C, 1980. “Theory of edge detection” Proc. Roy Soc B 207 pp. 187–217.CrossRefGoogle Scholar
Nazif, A and Levine, M, 1984. “Low level image segmentation: an expert system” IEEE PAMI-6 No. 5 pp. 555–577.CrossRefGoogle Scholar
Connell, J H and Brady, M, 1987. “Generating and generalising models of visual objects” Artificial Intelligence 31(2) pp. 159–184.CrossRefGoogle Scholar
Grimson, W E, 1986. “Recognition of object families using parametized models.” Proceedings of First International Conference on Computer Vision, IEEE Computer Society Press, 1987 pp. 93–101.Google Scholar
Marr, D and Nishihara, H K, 1978. “Representation and recognition of the spatial organisation of three dimensional shapes” Proc. Roy Soc London B 200 pp. 269–294.CrossRefGoogle Scholar
Pentland, A P, 1986. “Parts: structured descriptions of shape” Proceedings of AAAI, Pittsburgh pp. 695–701.Google Scholar