Skip to main content

Advertisement

SpringerLink
Log in

Seeded ND medical image segmentation by cellular automaton on GPU

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

We present a GPU-based framework to perform organ segmentation in N-dimensional (ND) medical image datasets by computation of weighted distances using the Ford–Bellman algorithm (FBA). Our GPU implementation of FBA gives an alternative and optimized solution to other graph-based segmentation techniques.

Methods

Given a number of K labelled-seeds, the segmentation algorithm evolves and segments the ND image in K objects. Each region is guaranteed to be connected to seeds with the same label. The method uses a Cellular Automata (CA) to compute multiple shortest-path-trees based on the FBA. The segmentation result is obtained by K-cuts of the graph in order to separate it in K sets. A quantitative evaluation of the method was performed by measuring renal volumes of 20 patients based on magnetic resonance angiography (MRA) acquisitions. Inter-observer reproducibility, accuracy and validity were calculated and associated computing times were recorded. In a second step, the computational performances were evaluated with different graphics hardware and compared to a CPU implementation of the method using Dijkstra’s algorithm.

Results

The ICC for inter-observer reproducibility of renal volume measurements was 0.998 (0.997–0.999) for two radiologists and the absolute mean difference between the two readers was lower than 1.2% of averaged renal volumes. The validity analysis shows an excellent agreement of our method with the results provided by a supervised segmentation method, used as reference.

Conclusions

The formulation of the FBA in the form of a CA is simple, efficient and straightforward, and can be implemented in low cost vendor-independent graphics hardware. The method can efficiently be applied to perform organ segmentation and quantitative evaluation in clinical routine.

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

Similar content being viewed by others

References

  1. Vezhnevets V, Konouchine V, Moscow R (2005) “GrowCut”-interactive multi-label NDImage segmentation by cellular automata. In: Proceedings of Graphicon, Novosibirsk Akademgorodok, Russia, pp 150–156

  2. Bai X, Sapiro G (2007) A geodesic framework for fast interactive image and video segmentation and matting. In: IEEE 11th international conference on computer vision, pp 1–8

  3. Qu Y, Wong TT, Heng PA (2006) Manga colorization. Proc ACM SIGGRAPH 25: 1214–1220

    Article  Google Scholar 

  4. Konushin V, Vezhnevets V, Moscow R (2006) Interactive image colorization and recoloring based on coupled map lattices. In: Conference proceedings of Graphicon’2006 Novosibirsk Akademgorodok, Russia, pp 231–234

  5. Yin L, Jian S, Chi-Keung T, Heung-Yeung S (2004) Lazy snapping. In: ACM SIGGRAPH 2004 papers. ACM, Los Angeles

  6. Rother C, Kolmogorov V, Blake A (2004) GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph (TOG) 23: 309–314

    Article  Google Scholar 

  7. Mortensen EN, Barrett WA (1998) Interactive segmentation with intelligent scissors. Graph Models Image Process 60: 349–384

    Article  Google Scholar 

  8. Boykov Y, Jolly MP (2000) Interactive organ segmentation using graph cuts. In: Proceedings of the medical image computing and computer-assisted intervention, pp 276–286

  9. Xu N, Ahuja N, Bansal R (2007) Object segmentation using graph cuts based active contours. Comput Vis Image Underst 107: 210–224

    Article  Google Scholar 

  10. Protiere A, Sapiro G (2007) Interactive image segmentation via adaptive weighted distances. IEEE Trans Image Process 16: 1046

    Article  PubMed  Google Scholar 

  11. Chefd’hotel C, Sebbane A (2007) Random walk and front propagation on watershed adjacency graphs for multilabel image segmentation. In: IEEE 11th international conference on computer vision, pp 1–7

  12. Falcão AX, Stolfi J, de Alencar Lotufo R (2004) The image foresting transform: theory, algorithms, and applications. IEEE Trans Pattern Anal Mach Intell 26: 19–29

    Article  PubMed  Google Scholar 

  13. Sinop AK, Grady L (2007) A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: IEEE 11th international conference on computer vision, pp 1–8

  14. Boykov Y, Funka-Lea G (2006) Graph cuts and efficient NDImage segmentation. Int J Comput Vis 70: 109–131

    Article  Google Scholar 

  15. Grady L, Funka-Lea G (2004) Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials. In: ECCV2004 workshop, pp 230–245

  16. Raspe M, Muller S (2007) Using a GPU-based framework for interactive tone mapping of medical volume data. In: SIGRAD, vol 28

  17. Owens JD, Luebke D, Govindaraju N, Harris M, Kruger J, Lefohn AE, Purcell TJ (2007) A survey of general-purpose computation on graphics hardware. In: Computer graphics forum, pp 80–113

  18. Vineet V, Narayanan PJ (2008) CUDA cuts: fast graph cuts on the GPU. In: IEEE CVPR workshops, pp 1–8

  19. Dixit N, Keriven R, Paragios N (2005) GPU-Cuts: combinatorial optimisation, graphic processing units and adaptive object extraction. CERTIS, ENPC, Marne la Vallee, France

  20. Volkov V, Demmel J (2007) Using GPUs to accelerate the bisection algorithm for finding eigenvalues of symmetric tridiagonal matrices. Electrical Engineering and Computer Sciences, University of California, Berkeley

  21. Aharon S, Grady L, Schiwietz T (2005) GPU accelerated isoperimetric algorithm for image segmentation, digital photo and video editing. In: Google Patents

  22. Von Neumann J, Burks AW (1966) Theory of self-reproducing automata. University of Illinois Press, Urbana

    Google Scholar 

  23. Wolfram S (2002) A new kind of science. Wolfram Media, Champaign

    Google Scholar 

  24. Ganguly N, Sikdar BK, Deutsch A, Canright G, Chaudhuri PP (2003) A survey on cellular automata. Dresden University of Technology, Technical Report Centre for High Performance Computing

  25. Gardner M (1970) Mathematical games: the fantastic combinations of John Conway’s New Solitaire Game ‘Life’. Sci Am 223: 120–123

    Article  Google Scholar 

  26. Zhao Y (2008) Lattice Boltzmann based PDE solver on the GPU. Vis Comput 24: 323–333

    Article  CAS  Google Scholar 

  27. Gobron S, Devillard F, Heit B (2007) Retina simulation using cellular automata and GPU programming. Mach Vis Appl 18: 331–342

    Article  Google Scholar 

  28. Alonso Atienza F, Requena Carrión J, García Alberola A, Rojo Álvarez JL, SÁnchez Muñoz JJ, Martínez SÁnchez J, Valdés Chávarri M (2005) A probabilistic model of cardiac electrical activity based on a cellular automata system. Revista Española de Cardiología (Internet) 58: 41–47

    Article  Google Scholar 

  29. Bellman R, Rand Corp Santa Monica Calif (1958) On a rooting problem. Q Appl Math 16:87–90

    Google Scholar 

  30. Ford LR, Fulkerson DR (1956) Maximal flow through a network. Can J Math 8: 399–404

    Google Scholar 

  31. Nepomniaschaya AS (2001) An associative version of the Bellman–Ford algorithm for finding the shortest paths in directed graphs. In: Parallel computing technologies, vol 2127. Springer, Berlin, pp 285–292

  32. Kauffmann C, Piche N (2008) Cellular automaton for ultra-fast watershed transform on GPU. In: ICPR 2008. Tampa bay, FL, USA, pp 1–4

  33. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1: 269–271

    Article  Google Scholar 

  34. Yatziv L, Bartesaghi A, Sapiro G (2006) O (N) implementation of the fast marching algorithm. J Comput Phys 212: 393–399

    Article  Google Scholar 

  35. Even S (1979) Graph algorithms, vol 249. Computer Science Press, Rockville

    Google Scholar 

  36. Fung J, Mann S (2008) Using graphics devices in reverse: GPU-based image processing and computer vision. In: IEEE international conference on multimedia and expo, pp 9–12

  37. Gernot Z, Christian T, Ivo I, Marcus M, Art T, Hans-Peter S (2007) GPU-based light wavefront simulation for real-time refractive object rendering. In: ACM SIGGRAPH 2007 sketches. ACM, San Diego

  38. Koutis I (2008) Faster algebraic algorithms for path and packing problems. In: Proceedings of the 35th international colloquium on automata, languages and programming, Part I. Springer, Reykjavik

  39. Bolz J, Farmer I, Grinspun E, Schröoder P (2003) Sparse matrix solvers on the GPU: conjugate gradients and multigrid. In: International conference on computer graphics and interactive techniques, pp 917–924

  40. Owens JD, Houston M, Luebke D, Green S, Stone JE, Phillips JC (2008) GPU computing. In: Proceedings of the IEEE, vol 96(5), May 2008

Download references

Author information

Authors and Affiliations

  1. Department of Medical Imaging, Notre-Dame Hospital, CHUM, 1560 Sherbrooke East, Montreal, QC, H2L 4M1, Canada

    Claude Kauffmann

  2. Object Research System Inc. (ORS), 760 Saint-Paul ouest, suite 101, Montreal, QC, H3C 1M4, Canada

    Nicolas Piché

Authors
  1. Claude Kauffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicolas Piché
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claude Kauffmann.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kauffmann, C., Piché, N. Seeded ND medical image segmentation by cellular automaton on GPU. Int J CARS 5, 251–262 (2010). https://doi.org/10.1007/s11548-009-0392-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-009-0392-0

Keywords

Search

Navigation