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Seeded ND medical image segmentation by cellular automaton on GPU

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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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.

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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

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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
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  2. Nicolas Piché
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Corresponding author

Correspondence to Claude Kauffmann.

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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

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

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