Integrating Ant Colony Optimization with Level Set Method for Biomedical Image Boundary Detection

• Autorzy: Rahebi J. , Hardalaç F.

Warianty tytułu

PL

Zastosowanie optymalizacji kolonią mrówek i metody poziomic w wykrywaniu brzegów obrazów biomedycznych

• Języki publikacji: EN

Abstrakty

EN

In this paper, ACO-based level set method is introduced to tackle the biomedical image boundary detection problem. The proposed ACO based level set method boundary detection approach is able to construct a pheromone matrix that represents the boundary information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image, then this result is initial contour for zero level set function in boundary of image that is segmented. Furthermore, the movements of these ants steers by the local variation of the image’s intensity values that it cause the contour move toward the object and exactly found boundaries. ACO-based method determines the initial contour to reduce the iteration steps. Such improvements simplify level set manipulation and lead to more robust segmentation. Experimental results show that the proposed method is can preserve the detail of the object and can be used to reduce the capacity of more computational tasks in research.

PL

W artykule opisano zastosowanie optymalizacji ACO (ang. Ant Colony Optimization) i metody poziomic w wychwytywaniu naruszenia brzegów obrazów biomedycznych. Proponowana metoda wykrywania granic tworzy matrycę feromonów, reprezentujących informacje brzegowe dla każdego z pikseli obrazu, w oparciu o ruch mrówek poruszających się po nim. Dane te stanowią wartość początkową dla funkcji ustalającej poziomicę zerową granicy obrazu. Pozwala to na redukcję ilości iteracji algorytmu. Wyniki badań eksperymentalnych potwierdzają skuteczność działania metody.

Słowa kluczowe

EN

ant colony optimization; level set method; biomedical image analysis

PL

optymalizacja koloniami mrówek wielu typów; metoda poziomic; obraz biomedyczny

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 5
• Strony: 218--221
• Opis fizyczny: Bibliogr. 21 poz., rys.

Twórcy

autor

Rahebi J.

• Gazi University

autor

Hardalaç F.

• Gazi University

Bibliografia

• [1] V. Caselles, F. Catte, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numer. Math., vol. 66, no. 1, pp. 1–31, Dec. 1993.
• [2] R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation: A level set approach,” IEEE Trans. Pattern. Anal. Mach. Intell., vol. 17, no. 2, pp. 158–175, Feb. 1995.
• [3] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” Int. J. Comput. Vis., vol. 1, no. 4, pp. 321–331, Jan. 1987.
• [4] S. C. Zhu and A. Yuille, “Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation,” IEEE Trans. Pattern. Anal. Mach. Intell., vol. 18, no. 9, pp. 884–900, Sep. 1996.
• [5] C. Xu and J. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Trans. Image. Process., vol. 7, no. 3, pp. 359–369, Mar. 1998.
• [6] S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, “Gradient flows and geometric active contour models,” in Proc. 5th Int. Conf. Comput. Vis., 1995, pp. 810–815.
• [7] V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J. Comput. Vis., vol. 22, no. 1, pp. 61–79, Feb. 1997.
• [8] R. Kimmel, A. Amir, and A. Bruckstein, “Finding shortest paths on surfaces using level set propagation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 6, pp. 635–640, Jun. 1995.
• [9] J. Sethian, “Level Set Methods and Fast Marching Methods”, Cambridge, U.K.: Cambridge Univ. Press, 1999.
• [10] S. Osher and R. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2002.
• [11] J. Gomes and O. Faugeras, “Reconciling distance functions and level sets,” J. Vis. Commun. Image Represent., vol. 11, no. 2, pp. 209–223, Jun. 2000.
• [12] M. Weber, A. Blake, and R. Cipolla, “Sparse finite elements for geodesic contours with level-sets,” in Proc. Eur. Conf. Comput. Vis., 2004, pp. 391–404.
• [13] V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J. Comput. Vis., vol. 22, no. 1, pp. 61–79, Feb. 1997.
• [14] M. Dorigo, V. Maniezzo, and A. Colorni, Ant system: Optimization by a colony of cooperating agents, IEEE Trans. On Systems, Man and cybernetics, part B, vol. 26, pp. 29-41, feb.1996.
• [15] M. Dorigo and L. M. Gambardella, Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Trans. On Evolutionary Computation, vol. 1, pp. 53–66, Apr. 1997.
• [16] J. Tian, W. Yu, and S. Xie, “An Ant Colony Optimization Algorithm For Image Edge Detection”, IEEE Congress on Evolutionary Computation, pp. 751-756, June. 2008.
• [17] M. Dorigo, M. Birattari, and T. Stutzle, Ant colony optimization, IEEE Computational Intelligence Magazine, vol. 1, pp. 28–39, Nov.2006.
• [18] T. McInerney, D. Terzopoulos, “Deformable models in medical image analysis: a survey”, Medical Image Analysis 1 (1996) 91–108.
• [19] C. Li, C. Xu, C. Gui, M.D. Fox, Level set evolution without reinitialization: a new variational formulation, in: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). (2005) 430–436.
• [20] Y. Chen, H.D. Tagare, S. Thiruvenkadam, F. Huang, D. Wilson, K.S. Gopinath, et al., Using prior shapes in geometric active contours in a variational framework, International Journal of Computer Vision 50 (2002) 315–328.
• [21] http://brainweb.bic.mni.mcgill.ca/brainweb/