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Active Partition Based Medical Image Understanding with Self-Organised Competitive Spatch Eduction

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Języki publikacji
EN
Abstrakty
EN
Medical Image Understanding is a recently defined semantic oriented image recognition task. Its specific requirements, highlighting complex characteristics of recognised objects as well as indispensable use of human-level expert knowledge almost every step of data processing sets new requirements for implemented algorithms. This paper focuses on linguistic image description method, designed to segment low level, semantically coherent image regions and mine adjacency relations among them. Example method results on medical images are presented to specify some methods properties.
Rocznik
Strony
67--78
Opis fizyczny
Bibliogr. 14 poz.
Twórcy
autor
autor
  • Technical University of Łódź Institute of Information Technology ul. Wólczańska 215, 90-924 Łódź, Poland
Bibliografia
  • [1] Tomczyk, A., Pryczek, M., Walczak, S., Jojczyk, K., and Szczepaniak, P. S., Spatch Based Active Partitions with Linguistically Formulated Energy, Journal of Applied Computer Science, Vol. 18, No. 1, 2010, pp. 87-115.
  • [2] Tomczyk, A. and Szczepaniak, P. S., Contribution of Active Contour Approach to Image Understanding, In: Proceedings of IEEE International Workshop on Imaging Systems and Techniques - IST 2007, May 4-5, 2007, Krakow, Poland, 2007.
  • [3] Tadeusiewicz, R. and Ogiela, M., Medical Image Understanding Technology, Vol. 156 of Studies in Fuzziness and Soft Computing, Springer-Verlag, Berlin, Heidelberg, New York, 2004.
  • [4] Tadeusiewicz, R. and Ogiela, M. R., Medical pattern understanding based on cognitive linguistic formalisms and computational intelligence methods, In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2008, part of the IEEE World Congress on Computational Intelligence, WCCI 2008, Hong Kong, China, June 1-6, 2008, IEEE, 2008, pp. 1728-1732.
  • [5] Edelman, G. M., Neural Darwinism: The Theory of Neuronal Group Selection, Basic Books, New York, 1987.
  • [6] Pryczek, M., Neuronal Groups and Interrelations, In: Proceedings of the International Multiconference on Computer Science and Information Technology, October 20-22, 2008. Wisła, Poland, edited by T. P.-P. M. Ganzha, M. Paprzycki, Vol. 3, 2008, pp. 221-227.
  • [7] Tadeusiewicz, R., Ogiela, M., and Szczepaniak, P., Notes on a Linguistic Description as the Basis for Automatic Image Understanding, Int. J. Appl. Math. Comput. Sci., Vol. 19, No. 1, 2009, pp. 143-150.
  • [8] Ogiela, L., Ogiela, M. R., and Tadeusiewicz, R., Mathematical Linguistics in Cognitive Medical Image Interpretation Systems, J. Math. Imaging Vis., Vol. 34, No. 3, 2009, pp. 328-340.
  • [9] Gonzalez, R. and Woods, R., Digital Image Processing, Prentice-Hall Inc., New Jersey, 2002.
  • [10] Sonka, M., Hlavec, V., and Boyle, R., Image Processing, Analysis and Machine Vision, Chapman and Hall, Cambridge, 1994.
  • [11] Davies, E. R., Machine Vision, Theory, Algorithms, Practicalities, Elsevier, Morgan Kaufmann, San Francisco, 2005.
  • [12] Haykin, S., Neural Networks: A Comprechensive Foundation, Macmillan College Publishing Company, New York, 1994.
  • [13] Fritzke, B., A Growing Neural Gas Network Learns Topologies, In: Advances in Neural Information Processing Systems 7, [NIPS Conference, Denver, Colorado, USA, 1994], edited by G. Tesauro, D. S. Touretzky, and T. K. Leen, Vol. 7, MIT Press, 1994, pp. 625-632.
  • [14] Pryczek, M., Supervised Object Classification Using Adaptive Active Hypercontours with Growing Neural Gas Representation, Journal of Applied Computer Science, Vol. 16, No. 2, 2008, pp. 69-80.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD9-0018-0004
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