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Tytuł artykułu

Application of active contours with expert knowledge to heart ventricle segmentation

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Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
Automatic heart ventricle segmentation in CT heart images can be an element of system supporting pulmonary embolism diagnosis. To solve that problem in this paper an application of two classical active contour models, snakes and geometric active contours, is proposed. The prepared implementation uses the unified model of those techniques which allows to define forces acting upon a contour only once. The nature of the images causes that the process of force construction requires additional expert knowledge since using only the information visible in the image satisfactory results cannot be obtained.
Rocznik
Strony
181--194
Opis fizyczny
Bibliogr. 22 poz.
Twórcy
autor
  • Institute of Information Technology, Lodz University of Technology, ul. Wolczanska 215, Lodz, Poland
Bibliografia
  • [1] M. Kass, A. Witkin, D. Terzopoulos. Snakes: Active contour models . International Journal of Computer Vision, pp. 321–331, 1988.
  • [2] L. D. Cohen. On active contour models and balloons . Computer Vision, Graphics, and Image Processing. Image Understanding, 53(2):211–218, 1991.
  • [3] J. Ivins, J. Porrill. Active region models for segmenting medical images . IEEE International Conference on Image Processings, pp. 227–231, 1994.
  • [4] C. Xu, J. Prince. Snakes, shapes, and gradient vector flow . IEEE Transactions on Image Processing, 7(3):359–369, 1998.
  • [5] A. Amini, T. E. Weymouth, R. C. Jain. Using dynamic programming for solving variatioal problems in vision . IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(9):855–867, 1990.
  • [6] T. McInerney, D. Terzopoulos. Topologically adaptable snakes . Proceedings of International Conference on Computer Vision, pp. 840–845, 1995.
  • [7] Ch. Xu, A. Yezzi, J. Prince. On the relationship between parametric and geometric active contours . Proceedings of 34th Asilomar Conference on Signals, Systems and Computers, pp. 483–489, 2000.
  • [8] V. Casseles, F. Catte, T. Coll, F. Dibos. A geometric model for active contours in image processing . Numerische Mathematic, 66:1–31, 1993.
  • [9] R. Malladi, J. A. Sethian, B. C. Vemuri. A fast level set based algorithm for topology-independent shape modeling . Math. Imaging and Vision, 6(2):269–290, 1996.
  • [10] V. Caselles, R. Kimmel, G. Sapiro. Geodesic active contours . International Journal of Computer Vision, 22(1):61–79, 1997.
  • [11] V. Caselles. Geometric models for active contours . Proceedings of International Conference on Image Processing, vol. 3, pp. 9–12. IEEE, 1995.
  • [12] K. Siddiqi, Y. Lauziere, A. Tannenbaum, S. Zucker. Area and lengthminimizing flows for shape segmentation . IEEE Transactions on Image Processing, pp. 44–443, 1997.
  • [13] T. Chan, L. Vese. Active contours without edges . IEEE Transactions on Image Processing, 10(2):266–277, 2001.
  • [14] T. F. Cootes, C. J. Taylor. Active shape models - smart snakes. Proceedings Bibliografia 161 of 3rd British Machine Vision Conference, pp. 266–275. Springer-Verlag, 1992.
  • [15] T. Cootes, C. Taylor, D. Cooper, J. Graham. Active shape models – their training and application. CVGIP Image Understanding, 61(1):38–59, 1994.
  • [16] T. F. Cootes, G. J. Edwards, C. J. Taylor. Active appearance models. Lecture Notes in Computer Science, 1407:484–500, 1998.
  • [17] R. Grzeszczuk, D. Levin. Brownian strings: Segmenting images with stochastically deformable models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(10):1100–1113, 1997.
  • [18] M. Jacob, T. Blu, M. Unser. A unifying approach and interface for splinebased snakes. Proceedings of SPIE Med. Imaging, pp. 340–347, 2001.ave
  • [19] J. Schnabel, S. Arridge. Active contour models for shape description using multiscale di ff erential invariants. D. Pycock, eds., Proceedings of British Machine Vision Conference, pp. 197–206, 1995.
  • [20] J. Denzler, H. Niemann. Active rays: A new approach to contour tracking. International Journal of Computing and Information Technology, 4:9–16, 1996.
  • [21] A. Tomczyk, P. S. Szczepaniak, Knowledge Extraction for Heart Image Segmentation, Computer Recognition Systems 4, R. Burduk, M. Kurzyński, M. Woźniak, A. ˙Zołnierek (Eds.), Advances in Intelligent and Soft Computing, vol. 95, pp. 579-586, Springer-Verlag, 2011.
  • [22] A. Tomczyk, P. S. Szczepaniak, Adaptive potential active contours, Pattern Analysis and Application, vol. 14, pp. 425-440, Springer-Verlag, London, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bb2ad7e9-815c-4043-a9a8-3a13641fdf28
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