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Image Segmentation Using Color Information and Its Application in Colonscopy

  • Conference paper
Book cover Communicating with Virtual Worlds

Part of the book series: CGS CG International Series ((3056))

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Abstract

An image segmentation algorithm, based on human colour perception, has been designed and implemented. An image in the RGB space is obtained through a conventional frame grabber. It is transformed into a perceptual colour space (HSI) and a histogram is constructed to estimate the size of any required feature. A regular decomposition of the image is then made, in which each node contains statistical information about the colour attributes of the pixels in the corresponding region. The best node is selected as a seed, and a merging process then obtains the boundary of the region. The algorithm has been tested on the identification of fluid in colon images observed through a conventional endoscope. In the intended application the segmentation will be part of a control system, and will enable the instrument to suck fluid out of the human colon automatically. Preliminary results suggest that a fast and accurate segmentation can be obtained using simple preceptual colour criteria. Real time performance could be achieved by implementing the algorithm on a small pyramid architecture.

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References

  • Akira Shiozaki, Edge Extraction Using Entropy Operator, Computer Vision, graphics, and Image Processing 36, 1.9.1986.

    Article  Google Scholar 

  • Ali M., W. N. Martin, and J. K. Aggarwal, Color-based computer Analysis of Aerial Photographs, Computer Graphics and image Processing 9,1979.

    Google Scholar 

  • Ballard, Dana H. and Christopher M. Brown, Computer Vision, Prentic-Hall, New Yark, 1981.

    Google Scholar 

  • Billmeyer, F. W. and M. Saltzman, Principles of Color Technology, Wiley, New York, 1981.

    Google Scholar 

  • Borrow, H.G., Popplestone, R.J., Relational Description of picture Processing, Machine Intelligence Vol. 6,1971.

    Google Scholar 

  • Brice, C.R., Fennema, C.L., Scene Analysis Using Regions, Artificial Intelligence, 1970.

    Google Scholar 

  • Burger, P. and Gillies D., Interactive Computer Graphics, Addison Wesley, Workingham, 1989.

    MATH  Google Scholar 

  • Celenk Mehemet, A Color clustering Technique for Image Segmentation, Computer Vision, Graphics, and Image Processing 52,145–170 (1990).

    Article  Google Scholar 

  • Chamberlin, G. J. and Chamberlin D. G., Colour its Measurement, Computation and Application, Heyden, London, 1980.

    Google Scholar 

  • Conners R. W., et al, A Theoretical Comparison of Texture algorithms, IEEE Trans. Pattern Anal, machine Intell, vol. PAMI-2 1980.

    Google Scholar 

  • Dastous, F. et al., Texture Discrimination based on detailed measure of the power spectrum, Proc. of the 7th International Conference on Pattern recognition, Montreal, Canada, July 30 – Aug. 2,83–86.

    Google Scholar 

  • Davis L. et al., Texture Analysis Using Generalized Co-occurrence Matrices, in pattern Recognition and Image Processing conference, Chicogo, IL.

    Google Scholar 

  • Faugéras, Olivier D., Digital Colour Image Processing within the framwork of a Human Visual Model, IEEE transactions on Acoustics, Speech, and Signal Processing Vol. ASSP-27, August 1977.

    Google Scholar 

  • Galloway M, Texture Analysis Using Grey Level Run Length, Computer Graphics and Image Processing, vol.4, 1974.

    Google Scholar 

  • Gonzalez Rafael C, and Richard C Woods, Digital Image processing, Addison-Wesley, 1992.

    Google Scholar 

  • Haralic R. M, Statistical and Structural Approaches to Texture, proc. of IEEE, vol.67, 1979.

    Google Scholar 

  • Hiroshi T, Shuichi Nishio, A Study of Image Segmentation using a Perceptual Color System, SPIE Vol. 1607 Intelligent Robots and Computer Vision, 1991.

    Google Scholar 

  • Horowitz, S.L., Pavilids, Picture Segmentation by Directed Split and Merge Procedure, Proc. of the Second International Joint Conference on Pattern Recognition, Aug., 1974.

    Google Scholar 

  • Joann M, Taylor, Gerald M. Murch, and Paul A. McManus, A Uniform Perceptual Color System for Display Users, Proceedings of the SID, Vol. 30/1,1989.

    Google Scholar 

  • Jollands, David Ed. by, Sight, Light, and Colour, Cambridge, London, 1984.

    Google Scholar 

  • Julesz B., et al, The Fundamental Elements in Preattentive Vision and Perception of Texture, The Bell Syst Tech J, vol.62, 1983.

    Google Scholar 

  • Khan, Gul Nawas, Machine Vision for Endoscope control and Navigation, Ph.D Thesis, Imperial College of Science, Technology and Medicine, London, 1989.

    Google Scholar 

  • MacDonald, Lindsay W and Stephen A R Scrivener, Colours in the Mind, Presented at Computer Graphics, 1989.

    Google Scholar 

  • Marr D., Vision, W.H. Freeman, 1982.

    Google Scholar 

  • Muerle, J.L., Allen, D.C., Experimental Evaluation of Techniques for Automatic Segmentation of objects in a Complex Scene, Thompson Washington, 1968.

    Google Scholar 

  • Ohlander R, Keith Price, and D. Raj Reddy, Picture Segmentation Using a Recursive Region Splitting Method, Computer Graphics and image processing 8, 1978.

    Google Scholar 

  • Ohta Yu-ichi, Takeo Kanade, and Toshiyukai sakai, Colour Information for Region Segmentation, Computer graphics and Image Processing 13,1980.

    Google Scholar 

  • Pavlidis T’., Algorithms for Graphics and Image processing, Spriger-Verlag, 1982.

    Book  Google Scholar 

  • Rashid, Haroon, Shape from Shading and Motion Parameter Estimation Under Near Light Source Illumination, Ph.D Thesis, Imperial College of Science, Technology and Medicine, London, 1991.

    Google Scholar 

  • Sarabi Alireza and J. K. Aggarwal, Segmentation of Chromatic Images, Pattern recognition Vol. 13, No. 6, 1981.

    Google Scholar 

  • Shacter B., L. S. Davis, and A. Rosenfeld, Scene Segmentation by Detection in Color spaces, SIGART Newsletter No. 58 June 1976.

    Google Scholar 

  • Shoji Tominaga, Colour Image Segmentation using Three Perceptual Attributes, Proceedings IEEE, 1986.

    Google Scholar 

  • Song De Ma and A. Gagalowicz, Natural Texture Synthesis with the Control of the Autocorrelation and Histogram Parameters, Proc. of 3rd Scandinavian Conf. on Image analysis, July 1983.

    Google Scholar 

  • Sucar L E, Probabilistic Reasoning in Knowledge-Based Vision Systems, Ph.D Thesis, Imperial College of Science, Technology and Medicine, London, 1991.

    Google Scholar 

  • Travis, David, Effective Color Displays Theory and Practice, Academic, London, 1991.

    Google Scholar 

  • Weszka J. S., et al., A Comparative Study of Texture Measure for Terrain Classification, IEEE Trans. Syst., Man, Cybern., vol.SMC-6, Apr. 1976.

    Google Scholar 

  • Wyszecki, Gunter and W. S. Stiles, Color Science: Concepts and Methods, Quantitation Data and Formulae, Wiley, New York, 1982.

    Google Scholar 

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© 1993 Springer-Verlag Tokyo

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Ismaili, I.A., Gillies, D.F. (1993). Image Segmentation Using Color Information and Its Application in Colonscopy. In: Thalmann, N.M., Thalmann, D. (eds) Communicating with Virtual Worlds. CGS CG International Series. Springer, Tokyo. https://doi.org/10.1007/978-4-431-68456-5_49

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  • DOI: https://doi.org/10.1007/978-4-431-68456-5_49

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-68458-9

  • Online ISBN: 978-4-431-68456-5

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