Skip to main content
Springer Nature Link
Log in

A GPU-based elastic shape registration approach in implicit spaces

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

Abstract

In this paper, we present a GPU-based implementation of an elastic shape registration approach in implicit spaces. Shapes are represented using signed distance functions, while deformations are modeled by cubic B-splines. In a variational framework, an incremental free form deformation strategy is adopted to handle smooth deformations through an adaptive size control lattice grid. The grid control points are estimated by a closed-form solution which avoids the gradient descent iterations. However, even this solution is very far from real time. We show in detail that such an algorithm is computationally expensive with a time complexity of \({\mathbf O} (NCP_xNCP^2X^2Y^2)\) where \(NCP_x\) and NCP are the grid lattice resolution parameters in the shape domain of size \(X\times Y\). Moreover, the problem becomes more time-consuming with the increase in the number of control points because this requires the execution of the incremental algorithm several times. The closed-form solution was implemented using eight different GPU techniques. Our experimental results demonstrate speedups of more than \(150{\times}\) compared to the \(\texttt {C}\) implementation on a CPU.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Huang, X., Paragios, N., Metaxas, D.N.: Shape registration in implicit spaces using information theory and free form deformations. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1303–1318 (2006)

    Article  Google Scholar 

  2. Diciotti, S., Lombardo, S., Falchini, M., Picozzi, G., Mascalchi, M.: Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. IEEE Trans. Biomed. Eng. 58(12), 3418–28 (2011)

    Article  Google Scholar 

  3. El Abd Munim, H.E., Farag, A.A., Farag, A.A.: Shape representation and registration in vector implicit spaces: adopting a closed-form solution in the optimization process. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 763–768 (2013)

    Article  Google Scholar 

  4. Farag, A.A., Abd El Munim, H.E., Graham, J.H., Farag, A.A.: A novel approach for lung nodules segmentation in chest CT using level sets. IEEE Trans. Image Process. 22(12), 5202–5213 (2013)

    Article  MathSciNet  Google Scholar 

  5. Aslan, M.A., Shalaby, A., El Abd Munim, A.A., Farag, A.A.: Probabilistic shape-based segmentation method using level sets. Comput. Vis. (IET) 8(3), 182–194 (2014)

    Article  Google Scholar 

  6. Sahillioglu, Y., Kavan, L.: Skuller: a volumetric shape registration algorithm for modeling skull deformities. Med. Image Anal. 23(1), 15–27 (2015)

    Article  Google Scholar 

  7. Dokken, T., Hagen, T.R., Hjelmervik, J.M.: The GPU as a high performance computational resource. In: Proceedings of the 21st Spring Conference on Computer Graphics (SCCG), pp. 21–26. ACM (2005)

  8. Harris, M., Sengupta, S., Owens, J.D.: Parallel Prefix Sums (Scan) with Cuda, GPU Gems 3. Addison-Wesley, New York (2007)

    Google Scholar 

  9. Hwu, W.-M.: GPU Computing Gems, Jade Edition, vol. 2, 1st edn. Morgan Kaufmann/Elsevier, Burlington (2012)

    Chapter  Google Scholar 

  10. Glaskowsky, P.N.: NVIDIAs Fermi: The First Complete GPU Computing Architecture. NVIDIA Corporation, Whitepaper (2009)

  11. Kainz, B., Steinberger, M., Wein, W., Kuklisova-Murgasova, M., Malamateniou, C., Keraudren, K., Torsney-Weir, T., Rutherford, M., Aljabar, P., Hajnal, J.V., Rueckert, D.: Fast volume reconstruction from motion corrupted stacks of 2D slices. IEEE Trans. Med. Imaging 34(9), 1901–1913 (2015)

    Article  Google Scholar 

  12. Linjia, H., Nooshabadi, S., Ahmadi, M.: Massively parallel KD-tree construction and nearest neighbor search algorithms. In: IEEE International Symposium on circuits and systems (ISCAS), pp. 2752–2755 (2015)

  13. Ahn, I.J., Kim, J.H., Chang, Y.J., Jeong, K.Y., Beom Ra, J.: LOR-based reconstruction for super-resolved 3D PET image on GPU. IEEE Trans. Nucl Sci 62(3), 859–868 (2015)

    Article  Google Scholar 

  14. Guerriero, A., Anelli, V.W., Pagliara, A., Nutricato, R., Nitti, D.O.: Efficient implementation of InSAR time-consuming algorithm kernels on GPU environment, geoscience and remote sensing symposium (IGARSS), pp. 4264–4267. IEEE International (2015)

  15. Lin Yong, Du, Zhi Zhou, E.Y., Thomas, N.L.: An efficient parallel approach for sclera vein recognition. IEEE Trans. Inf. Forensics Secur 9(2), 147–157 (2014)

    Article  Google Scholar 

  16. Saponara, S., et al.: A multi-processor NoC-based architecture for real-time image/video enhancement. J Real Time Image Process 8(1), 111–125 (2013)

    Article  Google Scholar 

  17. Saponara, S., et al.: Motion estimation and CABAC VLSI co-processors for real-time high-quality H. 264/AVC video coding. Microprocess. Microsyst. 34(7), 316–328 (2010)

    Article  Google Scholar 

  18. Schenke, S., Wunsche, B.C.: GPU-Based Volume Segmentation. Image and Vision Computing, New Zealand (2005)

  19. Kohn, A., Drexl, J., Ritter, F., Konig, M., Peitgen, H.O.: GPU Accelerated Image Registration in Two and Three Dimensions, Bildverarbeitung fur die Medizin, pp. 261–265. Springer, Heidelberg (2006)

    Google Scholar 

  20. Saxena, V., Rohrer, J., Gong, L.: A parallel GPU algorithm for mutual information based 3D nonrigid image registration. Lecture Notes in Computer Science, Euro-Par 6272, 223–234 (2010)

  21. Shams, R., Sadeghi, P., Kennedy, R.A., Hartley, R.I.: A survey of medical image registration on multicore and the GPU. IEEE Sig. Process. Mag. 50 (2010)

  22. Cao, T., Tang, K., Mohamed, A., Tan, T.: Parallel banding algorithm to compute exact distance transform with the GPU. In: Proceedings of the ACM SIGGRAPH symposium on interactive 3D graphics and games, pp. 83–90 (2010)

  23. Fulkerson, B., Soatto, S.: Really quick shift: image segmentation on a GPU. In: Proceedings of the workshop on computer vision using GPUs (2010)

  24. Narayanaswamy, A., Dwarakapuram, S., Bjornsson, C., Cutler, B., Shain, W., Roysam, B.: Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation. IEEE Trans. Med. Imaging 29, 583–597 (2010)

    Article  Google Scholar 

  25. Ruijters, D., ter Haar Romeny, B.M., Suetens, P.: GPU-accelerated elastic 3D image registration for intra-surgical applications. Comput. Methods Prog. Biomed. 103, 104–112 (2011)

    Article  Google Scholar 

  26. Park,.I.K., Singhal, N., Lee, M.H., Cho, S., Kim, C.W.: Design and performance evaluation of image processing algorithms on GPUs. IEEE Trans. Parallel Distrib. Syst. 2(1), 91–104 (2011)

    Article  Google Scholar 

  27. Broxvall, M., Emilsson, K., Thunberg, P.: Fast GPU based adaptive filtering of 4D echocardiography. IEEE Trans. Med. Imaging 31, 1165–1172 (2012)

    Article  Google Scholar 

  28. Collins, M., Xu, J., Grady, L., Singh, V.: Random walks based multiimage segmentation: quasiconvexity results and GPU-based solutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1656–1663 (2012)

  29. Gomez-Luna, J., Gonzalez-Linares, J., Benavides, J., Guil, N.: An optimized approach to histogram computation on GPU. Mach. Vis. Appl. 24(5), 899–908 (2013)

    Article  Google Scholar 

  30. Ruijters, D., Thevenaz, P.: GPU prefilter for accurate cubic B-spline interpolation. Comput. J. 55(1), 15–20 (2012)

    Article  Google Scholar 

  31. Otake, Y., Armand, M., Armiger, R.S., Kutzer, M.D., Basafa, E., Kazanzides, P., Taylor, R.H.: Intraoperative Image-based multiview 2D/3D registration for image-guided orthopaedic surgery: incorporation of fiducial-based C-arm tracking and GPU-acceleration. IEEE Trans. Med. Imaging 31(4), 948–962 (2012)

    Article  Google Scholar 

  32. Ikeda, K., Ino, F., Hagihara, K.: Efficient acceleration of mutual information computation for nonrigid registration using CUDA. IEEE J. Biomed. Health Inform. 18(3), 956–968 (2014)

    Article  Google Scholar 

  33. Passerone, C., Sansoe, C., Maggiora, R., Avolio, C., Zavagli, M., Minati, F., Costantini, M.: Highly parallel image co-registration techniques using GPUs. In: Aerospace Conference, IEEE, pp. 1–12 (2014)

  34. Gruslys, A., Acosta-Cabronero, J., Nestor, P.J., Williams, G.B., Ansorge, R.E.: A new fast accurate nonlinear medical image registration program including surface preserving regularization. IEEE Trans. Med. Imaging 33(11), 2118–2127 (2014)

    Article  Google Scholar 

  35. Choy, C.B., Stark, M., Corbett-Davies, S., Savarese, S.: Enriching object detection with 2D-3D registration and continuous viewpoint estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2512–2520 (2015)

  36. Valero-Lara, P.: Multi-GPU acceleration of DARTEL (early detection of Alzheimer). In: IEEE International Conference onCluster Computing (CLUSTER), pp. 346–354 (2014)

  37. Ibragimov, B., et al.: Shape representation for efficient landmark-based segmentation in 3-D. IEEE Trans. Med. Imaging 33(4), 861–874 (2014)

    Article  Google Scholar 

  38. Allusse, Y., Horain, P., Agarwal, A., Saipriyadarshan, C.: GpuCV: an opensource GPU-accelerated framework for image processing and computer vision. In: Proceedings of ACM International Conference, Multimedia, pp. 1089–1092 (2008)

  39. Babenko, P., Shah, M.: MinGPU: a minimum GPU library for computer vision. Real Time Image Process. 3(4), 255–268 (2008)

    Article  Google Scholar 

  40. Fung, J., Mann, S., Aimone, C.: OpenVIDIA: parallel GPU computer vision. In: Proceedings of ACM International Conference, Multimedia, pp. 849–852 (2005)

  41. Yousef, A.H., Abd El Munim, H.E.: An accelerated shape based segmentation approach adopting the pattern search optimizer. Ain Shams Eng. J. (2016). doi:10.1016/j.asej.2016.11.002

    Article  Google Scholar 

  42. Kirk, D.B., Hwu, W.W.: Programming Massively Parallel Processors, NVIDIA. Morgan Kaufmann, San Francisco (2010)

    Google Scholar 

  43. https://www.nvidia.com/content/PDF/kepler/Tesla-K20-Passive-BD-06455-001-v05.pdf

  44. http://www.geforce.com/hardware/notebook-gpus/geforce-gt-720m/specifications

  45. http://www.geforce.com/hardware/desktop-gpus/geforce-gt-610/specifications

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Systems Engineering, Faculty of Engineering, Ain Shams University, Cairo, Egypt

    Ahmed Hassan Yousef & Hossam E. Abd El Munim

Authors
  1. Ahmed Hassan Yousef
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hossam E. Abd El Munim
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Hossam E. Abd El Munim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yousef, A.H., Abd El Munim, H.E. A GPU-based elastic shape registration approach in implicit spaces. J Real-Time Image Proc 16, 2059–2071 (2019). https://doi.org/10.1007/s11554-017-0710-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-017-0710-7

Keywords