Skip to main content

Advertisement

SpringerLink
Log in

Fast and accurate circle tracking using active contour models

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

In this paper, we deal with the problem of circle tracking across an image sequence. We propose an active contour model based on a new energy. The center and radius of the circle is optimized in each frame by looking for local minima of such energy. The energy estimation does not require edge extraction, it uses the image convolution with a Gaussian kernel and its gradient which is computed using a GPU–CUDA implementation. We propose a Newton–Raphson type algorithm to estimate a local minimum of the energy. The combination of an active contour model which does not require edge detection and a GPU–CUDA implementation provides a fast and accurate method for circle tracking. We present some experimental results on synthetic data, on real images, and on medical images in the context of aorta vessel segmentation in computed tomography (CT) images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. ACR Foundation: Acr appropriateness criteria (2014). https://acsearch.acr.org/list

  2. Alemán-Flores, M., Alvarez, L., Caselles, V.: Texture-oriented anisotropic filtering and geodesic active contours in breast tumor ultrasound segmentation. J. Math. Imaging Vis. 28(1), 81–97 (2007)

    Article  MathSciNet  Google Scholar 

  3. Alvarez, L., Baumela, L., Henriquez, P., Marquez-Neila, P.: Morphological snakes. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 2197–2202 (2010)

  4. Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, 2nd edn. Springer Publishing Company Incorporated, New York (2010)

    MATH  Google Scholar 

  5. Bascle, B., Deriche, R.: Features extraction using parametric snakes. In: Pattern Recognition, 1992. Vol. III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on, pp. 659–662. IEEE (1992)

  6. Brox, T., Kim, Y.J., Weickert, J., Feiden, W.: Fully-automated analysis of muscle fiber images with combined region and edge-based active contours. In: Handels, H., Ehrhardt, J., Horsch, A., Meinzer, H.P., Tolxdorff, T. (eds.) Bildverarbeitung fr die Medizin 2006, Informatik aktuell, pp. 86–90. Springer, Berlin (2006)

    Chapter  Google Scholar 

  7. Brox, T., Rousson, M., Deriche, R., Weickert, J.: Colour, texture, and motion in level set based segmentation and tracking. Image Vis. Comput. 28(3), 376–390 (2010)

    Article  Google Scholar 

  8. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  9. Catté, F., Lions, P.L., Morel, J.M., Coll, T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29(1), 182–193 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chan, T.F., Vese, L.A.: Active contours without edges. Trans. Imaging Proc. 10(2), 266–277 (2001). doi:10.1109/83.902291

    Article  MATH  Google Scholar 

  11. Cheng, J., Grossman, M., McKercher, T.: Professional CUDA C Programming. Wiley, Indianapolis (2014)

    Google Scholar 

  12. Davies, E.R.: The effect of noise on edge orientation computations. Pattern Recogn. Lett. 6(5), 315–322 (1987)

    Article  Google Scholar 

  13. De Fontes, F.P.X., Barroso, G.A., Coupé, P.: Real time ultrasound image denoising. J. Real-Time Image Process. 6(1), 15–22 (2011)

    Article  Google Scholar 

  14. Debreuve, E., Barlaud, M., Marmorat, J.P., Aubert, G.: Active Contour Segmentation with a Parametric Shape Prior: Link with the Shape Gradient. In: ICIP, IEEE, pp. 1653–1656 (2006)

  15. Gadeski, E., Fard, H.O., Le Borgne, H.: Gpu deformable part model for object recognition. J. Real-Time Image Process., 1–13 (2014)

  16. Havel, J., Dubská, M., Herout, A., Josth, R.: Real-time detection of lines using parallel coordinates and cuda. J. Real-Time Image Process. 9(1), 205–216 (2014)

    Article  Google Scholar 

  17. Herout, A., Josth, R., Juránek, R., Havel, J., Hradis, M., Zemcík, P.: Real-time object detection on cuda. J. Real-Time Image Process. 6(3), 159–170 (2011)

    Article  Google Scholar 

  18. Hough, P.: Method and means for recognizing complex patterns (1962). URL: http://www.google.co.in/patents/US3069654. Us patent 3,069,654

  19. Illingworth, J., Kittler, J.: A survey of the hough transform. Comput. Vis. Graph. Image Process. 44(1), 87–116 (1988)

    Article  Google Scholar 

  20. Ioannou, D., Huda, W., Laine, A.F.: Circle recognition through a 2d hough transform and radius histogramming. Image Vis. Comput. 17(1), 15–26 (1999)

    Article  Google Scholar 

  21. Jacob, M., Blu, T., Unser, M.: Efficient energies and algorithms for parametric snakes. IEEE Trans. Image Process. 13, 1231–1244 (2004)

    Article  Google Scholar 

  22. Jan Essbach, B.L., Nacke, C.: Hough transform: Serial and parallel implementations. Tech. rep. URL: http://www.essbach.org/wp-content/uploads/2013/05/Hough

  23. Kimme, C., Ballard, D., Sklansky, J.: Finding circles by an array of accumulators. Commun. ACM 18(2), 120–122 (1975)

    Article  MATH  Google Scholar 

  24. Kirk, D.B.: Programming Massively Parallel Processors: A Hands-on Approach, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2010)

    Google Scholar 

  25. Kumar, P., Singhal, A., Mehta, S., Mittal, A.: Real-time moving object detection algorithm on high-resolution videos using gpus. J. Real-Time Image Process., 1–17 (2013)

  26. Laborda, M.A.M., Moreno, E.F.T., del Rincón, J.M., Jaraba, J.E.H.: Real-time gpu color-based segmentation of football players. J. Real-Time Image Process. 7(4), 267–279 (2012)

    Article  Google Scholar 

  27. Lamas-Rodríguez, J., Heras, D.B., Arguello, F., Kainmueller, D., Zachow, S., Bóo, M.: Gpu-accelerated level-set segmentation. J. Real-Time Image Process., 1–15 (2013)

  28. Levenverg, K.: A method for the solution of certain non-linear problems in least-squares. Q. Appl. Math. 2(2), 164–168 (1944)

    Article  MathSciNet  Google Scholar 

  29. Marquez-Neila, P., Baumela, L., Alvarez, L.: A morphological approach to curvature-based evolution of curves and surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 2–17 (2014)

    Article  Google Scholar 

  30. NVIDIA, C.: Cuda c best practices guide. Technical report. URL: http://docs.nvidia.com/cuda/cuda-c-best-practices-guide

  31. Pao, D.C.W., Li, H.F., Jayakumar, R.: Shapes recognition using the straight line hough transform: theory and generalization. IEEE Trans. Pattern Anal. Mach. Intell. 14(11), 1076–1089 (1992)

    Article  Google Scholar 

  32. Paragios, N., Deriche, R.: Geodesic active regions and level set methods for motion estimation and tracking. Comput. Vis. Image Underst. 97(3), 259–282 (2005)

    Article  Google Scholar 

  33. Podlozhnyuk, V.: Image convolution with cuda. NVIDIA Corporation, Technical report (2007)

  34. Ptrucean, V., Gurdjos, P., von Gioi, R.: A parameterless line segment and elliptical arc detector with enhanced ellipse fitting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) Computer Vision ECCV 2012. Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp. 572–585 (2012)

  35. Trujillo-Pino, A., Krissian, K., Aleman-Flores, M., Santana-Cedres, D.: Accurate subpixel edge location based on partial area effect. Image Vision Comput. 31(1), 72–90 (2013)

    Article  Google Scholar 

  36. Tsuji, S., Matsumoto, F.: Detection of ellipses by a modified hough transformation. IEEE Trans. Comput. 27(8), 777–781 (1978)

    Article  Google Scholar 

  37. Ujaldon, M., Ruiz, A., Guil, N.: On the computation of the circle hough transform by a GPU rasterizer. Pattern Recogn. Lett. 29(3), 309–318 (2008)

    Article  Google Scholar 

  38. Wang, Y.K., Huang, W.B.: A cuda-enabled parallel algorithm for accelerating retinex. J. Real-Time Image Process. 9(3), 407–425 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Informática y Sistemas, CTIM: Centro de tecnologías de la Imagen, Universidad de Las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Pamas, Spain

    Carmelo Cuenca, Esther González, Agustín Trujillo, Julio Esclarín, Luis Mazorra & Luis Alvarez

  2. Centro Singular de Investigación en Tecnoloxías da Información (CITIUS), University of Santiago de Compostela, Santiago de Compostela, Spain

    Juan Antonio Martínez-Mera & Pablo G. Tahoces

  3. University Hospital Complex of Santiago de Compostela (CHUS), Santiago de Compostela, Spain

    José M. Carreira

Authors
  1. Carmelo Cuenca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Esther González
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Agustín Trujillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Julio Esclarín
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Luis Mazorra
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Luis Alvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Juan Antonio Martínez-Mera
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pablo G. Tahoces
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. José M. Carreira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luis Alvarez.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (MPG 6612 kb)

Supplementary material 1 ((MPG 6612 kb)

Supplementary material 1 ((MPG 7516 kb)

Supplementary material 1 ((MPG 9196 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cuenca, C., González, E., Trujillo, A. et al. Fast and accurate circle tracking using active contour models. J Real-Time Image Proc 14, 793–802 (2018). https://doi.org/10.1007/s11554-015-0531-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-015-0531-5

Keywords

Search

Navigation