Advertisement

Journal of Real-Time Image Processing

, Volume 16, Issue 6, pp 1891–1908 | Cite as

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

  • Carlos A. S. J. Gulo
  • Antonio C. Sementille
  • João Manuel R. S. TavaresEmail author
Survey Paper

Abstract

Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

Keywords

Medical imaging Image segmentation Image registration Image reconstruction 

Notes

Acknowledgements

The first author would like to thank the Universidade do Estado de Mato Grosso (UNEMAT), in Brazil, and the National Scientific and Technological Development Council (“Conselho Nacional de Desenvolvimento Cientí?fico e Tecnológico”—CNPq), process 234306/2014-9, grant with reference #2010/15691-0, for the support given. The authors gratefully acknowledge the funding received from Project NORTE-01-0145-FEDER-000022—SciTech—Science and Technology for Competitive and Sustainable Industries, co-financed by “Programa Operacional Regional do Norte” (NORTE2020), through “Fundo Europeu de Desenvolvimento Regional” (FEDER).

References

  1. 1.
    Adeshina, A.M., Hashim, R., Khalid, N.E.A., Abidin, S.Z.Z.: Locating abnormalities in brain blood vessels using parallel computing architecture. Interdiscip. Sci.-Comput. Life Sci. 4(3), 161–172 (2012).  https://doi.org/10.1007/s12539-012-0132-y CrossRefGoogle Scholar
  2. 2.
    Aitali, N., Cherradi, B., Abbassi, A.E., Bouattane, O., Youssfi, M.: Parallel implementation of bias field correction fuzzy c-means algorithm for image segmentation. Int. J. Adv. Comput. Sci. Appl. 7(3), 375–383 (2016)Google Scholar
  3. 3.
    Akgun, D., Sakoglu, U., Esquivel, J., Adinoff, B., Mete, M.: GPU accelerated dynamic functional connectivity analysis for functional MRI data. Comput. Med. Imaging Graph. 43, 53–63 (2015).  https://doi.org/10.1016/j.compmedimag.2015.02.009 CrossRefGoogle Scholar
  4. 4.
    Alvarado, R., Tapia, J.J., Rolon, J.C.: Medical image segmentation with deformable models on graphics processing units. J. Supercomput. 68(1), 339–364 (2014).  https://doi.org/10.1007/s11227-013-1042-4 CrossRefGoogle Scholar
  5. 5.
    Balla-Arabé, S., Gao, X.: Geometric active curve for selective entropy optimization. Neurocomputing 139, 65–76 (2014).  https://doi.org/10.1016/j.neucom.2013.09.058 CrossRefGoogle Scholar
  6. 6.
    Barros, R., Van Geldermalsen, S., Boers, A., Belloum, A., Marquering, H., Olabarriaga, S.: Heterogeneous platform programming for high performance medical imaging processing. Lecture Notes in Computer Science 8374 LNCS:301–310, (2014)  https://doi.org/10.1007/978-3-642-54420-0_30 CrossRefGoogle Scholar
  7. 7.
    Birk, M., Dapp, R., Ruiter, N., Becker, J.: GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography. J. Parallel Distrib. Comput. 74(1), 1730–1743 (2014).  https://doi.org/10.1016/j.jpdc.2013.09.007 CrossRefGoogle Scholar
  8. 8.
    Birk, M., Zapf, M., Balzer, M., Ruiter, N., Becker, J.: A comprehensive comparison of GPU- and FPGA-based acceleration of reflection image reconstruction for 3D ultrasound computer tomography. J. Real-Time Image Proc. 9(1, SI), 159–170 (2014).  https://doi.org/10.1007/s11554-012-0267-4 CrossRefGoogle Scholar
  9. 9.
    Blas, J.G., Abella, M., Isaila, F., Carretero, J., Desco, M.: Surfing the optimization space of a multiple-GPU parallel implementation of a X-ray tomography reconstruction algorithm. J. Syst. Softw. 95, 166–175 (2014).  https://doi.org/10.1016/j.jss.2014.03.083 CrossRefGoogle Scholar
  10. 10.
    Cai, Y., Guo, X., Zhong, Z., Mao, W.: Dynamic meshing for deformable image registration. Comput. Aided Des. 58(SI), 141–150 (2015).  https://doi.org/10.1016/j.cad.2014.08.009 MathSciNetCrossRefGoogle Scholar
  11. 11.
    Chen, Z., Chen, Y., Huang, Q.: Development of a wireless and near real-time 3D ultrasound strain imaging system. IEEE Trans. Biomed. Circuits Syst. 10(2), 394–403 (2016).  https://doi.org/10.1109/TBCAS.2015.2420117 MathSciNetCrossRefGoogle Scholar
  12. 12.
    Christensen, G.E.: MIMD vs. SIMD parallel processing: a case study in 3D medical image registration. Parallel Comput. 24, 1369–1383 (1998).  https://doi.org/10.1016/S0167-8191(98)00062-3 CrossRefGoogle Scholar
  13. 13.
    Chung, J., Sternberg, P., Yang, C.: High-performance three-dimensional image reconstruction for molecular structure determination. Int. J. High Perform. Comput. Appl. 24(2), 117–135 (2010).  https://doi.org/10.1177/1094342009106293 CrossRefGoogle Scholar
  14. 14.
    Crane, J., Crawford, F., Nelson, S.: Grid enabled magnetic resonance scanners for near real-time medical image processing. J. Parallel Distrib. Comput. 66(12), 1524–1533 (2006).  https://doi.org/10.1016/j.jpdc.2006.03.009 CrossRefGoogle Scholar
  15. 15.
    Daggett, T., Greenshields, I.: A cluster computer system for the analysis and classification of massively large biomedical image data. Comput. Biol. Med. 28(1), 47–60 (1998).  https://doi.org/10.1016/S0010-4825(97)00032-2 CrossRefGoogle Scholar
  16. 16.
    D’Amore, L., Casaburi, D., Marcellino, L., Murli, A.: Numerical solution of diffusion models in biomedical imaging on multicore processors. Int. J. BioMed. Imaging 2011(1), 1–16 (2011).  http://doi.org/10.1155/2011/680765 CrossRefGoogle Scholar
  17. 17.
    Dandekar, O., Shekhar, R.: FPGA-accelerated deformable image registration for improved target-delineation during CT-guided interventions. IEEE Trans. Biomed. Circuits Syst. 1(2), 116–127 (2007).  https://doi.org/10.1109/TBCAS.2007.909023 CrossRefGoogle Scholar
  18. 18.
    Deng, J., Yu, H., Ni, J., He, T., Zhao, S., Wang, L., Wang, G.: A parallel implementation of the Katsevich algorithm for 3-D CT image reconstruction. J. Supercomput. 38(1), 35–47 (2006).  https://doi.org/10.1007/s11227-006-6675-0 CrossRefGoogle Scholar
  19. 19.
    Domanski, L., Bednarz, T., Gureyev, T., Murray, L., Huang, B.E., Nesterets, Y., Thompson, D., Jones, E., Cavanagh, C., Wang, D., Vallotton, P., Sun, C., Khassapov, A., Stevenson, A., Mayo, S., Morell, M., George, A.W., Taylor, J.A.: Applications of heterogeneous computing in computational and simulation science. Int. J. Comput. Sci. Eng. 8(3), 240–252 (2013)Google Scholar
  20. 20.
    Doyley, M., Van Houten, E., Weaver, J., Poplack, S., Duncan, L., Kennedy, F., Paulsen, K.: Shear modulus estimation using parallelized partial volumetric reconstruction. IEEE Trans. Med. Imaging 23(11), 1404–1416 (2004).  https://doi.org/10.1109/TMI.2004.834624 CrossRefGoogle Scholar
  21. 21.
    Du, X., Dang, J., Wang, Y., Wang, S., Lei, T.: A parallel nonrigid registration algorithm based on b-spline for medical images. Comput. Math. Methods Med. 2016(1), 1–14 (2016).  http://doi.org/10.1155/2016/7419307 MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Eidheim, O., Skjermo, J., Aurdal, L.: Real-time analysis of ultrasound images using GPU. In: Lemke, H., Inamura, K., Doi, K., Vannier, M., Farman, A. (eds) CARS 2005: Computer Assisted Radiology and Surgery, International Congress Series, vol. 1281, pp. 284–289,  https://doi.org/10.1016/j.ics.2005.03.187, 19th International Congress and Exhibition on Computer Assisted Radiology and Surgery (2005)CrossRefGoogle Scholar
  23. 23.
    Eklund, A., Dufort, P., Forsberg, D., LaConte, S.M.: Medical image processing on the GPU-past, present and future. Med. Image Anal. 17(8), 1073–1094 (2013).  https://doi.org/10.1016/j.media.2013.05.008 CrossRefGoogle Scholar
  24. 24.
    Eklund, A., Dufort, P., Villani, M., LaConte, S.: BROCCOLI: software for fast fMRI analysis on many-core CPUs and GPUs. Front. Neuroinform. 8(24), 1–19 (2014).  https://doi.org/10.3389/fninf.2014.00024 CrossRefGoogle Scholar
  25. 25.
    El-Moursy, A.A., ElAzhary, H., Younis, A.: High-accuracy hierarchical parallel technique for hidden markov model-based 3D magnetic resonance image brain segmentation. Concurr. Comput.-Pract. Exp. 26(1), 194–216 (2014).  https://doi.org/10.1002/cpe.2959 CrossRefGoogle Scholar
  26. 26.
    Ellingwood, N.D., Yin, Y., Smith, M., Lin, C.L.: Efficient methods for implementation of multi-level nonrigid mass-preserving image registration on GPUs and multi-threaded CPUs. Comput. Methods Programs Biomed. 127, 290–300 (2016).  https://doi.org/10.1016/j.cmpb.2015.12.018 CrossRefGoogle Scholar
  27. 27.
    Fan, Z., Xie, Y.: A block-wise approximate parallel implementation for ART algorithm on CUDA-enabled GPU. Biomed. Mater. Eng. 26(1), S1027–S1035 (2015).  https://doi.org/10.3233/BME-151398 CrossRefGoogle Scholar
  28. 28.
    Formiconi, A., Passeri, A., Guelfi, M., Masoni, M., Pupi, A., Meldolesi, U., Malfetti, P., Calori, L., Guidazzoli, A.: World wide web interface for advanced SPECT reconstruction algorithms implemented on a remote massively parallel computer. Int. J. Med. Inform. 47, 125–138 (1997).  https://doi.org/10.1016/S1386-5056(97)00089-0 CrossRefGoogle Scholar
  29. 29.
    Gabriel, E., Venkatesan, V., Shah, S.: Towards high performance cell segmentation in multispectral fine needle aspiration cytology of thyroid lesions. Comput. Methods Programs Biomed. 98(3), 231–240 (2010).  https://doi.org/10.1016/j.cmpb.2009.07.008 CrossRefGoogle Scholar
  30. 30.
    Gallea, R., Ardizzone, E., Pirrone, R., Gambino, O.: Three-dimensional fuzzy kernel regression framework for registration of medical volume data. Pattern Recognit. 46(11), 3000–3016 (2013).  https://doi.org/10.1016/j.patcog.2013.03.025 CrossRefGoogle Scholar
  31. 31.
    Gao, Y., Yang, J., Xu, X., Shi, F.: Efficient cellular automaton segmentation supervised by pyramid on medical volumetric data and real time implementation with graphics processing unit. Expert Syst. Appl. 38(6), 6866–6871 (2011).  https://doi.org/10.1016/j.eswa.2010.12.049 CrossRefGoogle Scholar
  32. 32.
    Gates, M., Heath, M.T., Lambros, J.: High-performance hybrid CPU and GPU parallel algorithm for digital volume correlation. Int. J. High Perform. Comput. Appl. 29(1, SI), 92–106 (2015).  https://doi.org/10.1177/1094342013518807 CrossRefGoogle Scholar
  33. 33.
    Gebali, F.: Algorithms and Parallel Computing. Wiley, London (2011)CrossRefGoogle Scholar
  34. 34.
    Gulo, C.A.S.J., de Arruda, H.F., de Araujo, A.F., Sementille, A.C., Tavares, J.M.R.S.: Efficient parallelization on gpu of an image smoothing method based on a variational model. J. Real-Time Image Proc. (2016).  https://doi.org/10.1007/s11554-016-0623-x CrossRefGoogle Scholar
  35. 35.
    Hamdaoui, F., Sakly, A., Mtibaa, A.: FPGA implementation of particle swarm optimization based on new fitness function for MRI images segmentation. Int. J. Imaging Syst. Technol. 25(2), 139–147 (2015).  https://doi.org/10.1002/ima.22130 CrossRefGoogle Scholar
  36. 36.
    Heras, J.L.R.D.B., Arguello, F., Kainmueller, D., Zachow, S., Boo, M.: GPU-accelerated level-set segmentation. J. Real-Time Image Proc. 12(1), 15–29 (2016).  https://doi.org/10.1007/s11554-013-0378-6 CrossRefGoogle Scholar
  37. 37.
    Higgins, W.E., Swift, R.D.: Distributed system for processing 3D medical images. Comput. Biol. Med. 27(2), 97–115 (1997).  https://doi.org/10.1016/S0010-4825(96)00042-X CrossRefGoogle Scholar
  38. 38.
    Hu, J., Zhao, X., Zhang, H.: A GPU-based multi-resolution approach to iterative reconstruction algorithms in X-ray 3D dual spectral computed tomography. Neurocomputing 215(SI), 71–81 (2016).  https://doi.org/10.1016/j.neucom.2016.01.115 CrossRefGoogle Scholar
  39. 39.
    Jaros, M., Strakos, P., Karasek, T., Riha, L., Vasatova, A., Jarogova, M., Kozubek, T.: Implementation of K-means segmentation algorithm on Intel Xeon Phi and GPU: application in medical imaging. Adv. Eng. Softw. 103, 21–28 (2017).  https://doi.org/10.1016/j.advengsoft.2016.05.008 CrossRefGoogle Scholar
  40. 40.
    Johnsen, S.F., Taylor, Z.A., Clarkson, M.J., Hipwell, J., Modat, M., Eiben, B., Han, L., Hu, Y., Mertzanidou, T., Hawkes, D.J., Ourselin, S.: NiftySim: a GPU-based nonlinear finite element package for simulation of soft tissue biomechanics. Int. J. Comput. Assist. Radiol. Surg. 10(7), 1077–1095 (2015).  https://doi.org/10.1007/s11548-014-1118-5 CrossRefGoogle Scholar
  41. 41.
    Kalmoun, E.M., Kostler, H., Rude, U.: 3D optical flow computation using a parallel variational multigrid scheme with application to cardiac C-arm CT motion. Image Vis. Comput. 25(9), 1482–1494 (2007).  https://doi.org/10.1016/j.imavis.2006.12.017 CrossRefGoogle Scholar
  42. 42.
    Kegel, P., Schellmann, M., Gorlatch, S.: Using OpenMP vs. threading building blocks for medical imaging on multi-cores. Lecture Notes in Computer Science 5704 LNCS:654–665, (2009)  https://doi.org/10.1007/978-3-642-03869-3_62 Google Scholar
  43. 43.
    Kegel, P., Schellmann, M., Gorlatch, S.: Comparing programming models for medical imaging on multi-core systems. Concurr. Comput.-Pract. Exp. 23(10), 1051–1065 (2011).  https://doi.org/10.1002/cpe.1671 CrossRefGoogle Scholar
  44. 44.
    Kerr, J.P., Bartlett, E.B.: Medical image-processing utilizing neural networks trained on a massively-parallel computer. Comput. Biol. Med. 25(4), 393–403 (1995).  https://doi.org/10.1016/0010-4825(95)00017-X CrossRefGoogle Scholar
  45. 45.
    Kirk, D., Hwu, W.M.: Programming Massively Parallel Processors: A Hands-on Approach. Elsevier, Amsterdam (2010)Google Scholar
  46. 46.
    Koestler, H., Stuermer, M., Pohl, T.: Performance engineering to achieve real-time high dynamic range imaging. J. Real-Time Image Proc. 11(1), 127–139 (2016).  https://doi.org/10.1007/s11554-012-0312-3 CrossRefGoogle Scholar
  47. 47.
    Kumar, V., Rutt, B., Kurc, T., Catalyurek, U., Pan, T., Chow, S., Lamont, S., Martone, M., Saltz, J.: Large-scale biomedical image analysis in grid environments. IEEE Trans. Inf Technol. Biomed. 12(2), 154–161 (2008).  https://doi.org/10.1109/TITB.2007.908466 CrossRefGoogle Scholar
  48. 48.
    Lapeer, R.J., Shah, S.K., Rowland, R.S.: An optimised radial basis function algorithm for fast non-rigid registration of medical images. Comput. Biol. Med. 40(1), 1–7 (2010).  https://doi.org/10.1016/j.compbiomed.2009.10.002 CrossRefGoogle Scholar
  49. 49.
    Lee, D., Dinov, I., Dong, B., Gutman, B., Yanovsky, I., Toga, A.W.: CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms. Comput. Methods Programs Biomed. 106(3), 175–187 (2012).  https://doi.org/10.1016/j.cmpb.2010.10.013 CrossRefGoogle Scholar
  50. 50.
    Mafi, R., Sirouspour, S.: GPU-based acceleration of computations in nonlinear finite element deformation analysis. Int. J. Numer. Methods Biomed. Eng. 30(3), 365–381 (2014).  https://doi.org/10.1002/cnm.2607 CrossRefGoogle Scholar
  51. 51.
    Mahmoudi, S., Manneback, P.: Multi-CPU/multi-GPU based framework for multimedia processing. In: IFIP Advances in Information and Communication Technology, vol. 456, 54–65 (2015).  https://doi.org/10.1007/978-3-319-19578-0_5 Google Scholar
  52. 52.
    Melo, R., Falcao, G., Barreto, J.: Real-time HD image distortion correction in heterogeneous parallel computing systems using efficient memory access patterns. J. Real-Time Image Proc. 11(1), 83–91 (2016).  https://doi.org/10.1007/s11554-012-0304-3 CrossRefGoogle Scholar
  53. 53.
    Melvin, C., Xu, M., Thulasiraman, P.: HPC for iterative image reconstruction in CT, vol. 273, pp. 61–68 (2008).  https://doi.org/10.1145/1370256.1370265
  54. 54.
    Meng, B., Pratx, G., Xing, L.: Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment. Med. Phys. 38(12), 6603–6609 (2011).  https://doi.org/10.1118/1.3660200 CrossRefGoogle Scholar
  55. 55.
    Meng, L.: Acceleration method of 3D medical images registration based on compute unified device architecture. Bio-Med. Mater. Eng. 24(1), 1109–1116 (2014).  https://doi.org/10.3233/BME-130910 CrossRefGoogle Scholar
  56. 56.
    Mertes, J.G., Marranghello, N., Pereira, A.S.: Real-time module for digital image processing developed on a FPGA. Int. Fed. Autom. Control Proc. Volumes 46(28), 405–410 (2013).  https://doi.org/10.3182/20130925-3-CZ-3023.00072 CrossRefGoogle Scholar
  57. 57.
    Miller, M., Butler, C.: 3D maximum a posteriori estimation for single photon emission computed tomography on massively-parallel computers. IEEE Trans. Med. Imaging 12(3), 560–565 (1993).  https://doi.org/10.1109/42.241884 CrossRefGoogle Scholar
  58. 58.
    Moyano-Avila, E., Orozco-Barbosa, L., Quiles, F.J.: Parallel algorithms based on the temporal-window method for non-alternating 3D-WT over angiographies using a multicomputer. J. Signal Process. Syst. Signal Image Video Technol. 55(1–3), 267–279 (2009).  https://doi.org/10.1007/s11265-008-0188-4 CrossRefGoogle Scholar
  59. 59.
    Murphy, M., Alley, M., Demmel, J., Keutzer, K., Vasanawala, S., Lustig, M.: Fast l1 -SPIRiT compressed sensing parallel imaging MRI: scalable parallel implementation and clinically feasible runtime. IEEE Trans. Med. Imaging 31(6), 1250–1262 (2012).  https://doi.org/10.1109/TMI.2012.2188039 CrossRefGoogle Scholar
  60. 60.
    Nguyen, T.A., Nakib, A., Nguyen, H.N.: Medical image denoising via optimal implementation of non-local means on hybrid parallel architecture. Comput. Methods Programs Biomed. 129, 29–39 (2016).  https://doi.org/10.1016/j.cmpb.2016.02.002 CrossRefGoogle Scholar
  61. 61.
    Nguyena, T.A., Nakib, A., Nguyen, H.N.: Medical image denoising via optimal implementation of non-local means on hybrid parallel architecture. Comput. Methods Programs Biomed. 129, 29–39 (2016).  https://doi.org/10.1016/j.cmpb.2016.02.002 CrossRefGoogle Scholar
  62. 62.
    Nieto, A., Brea, V., Vilariño, D.L., Osorio, R.R.: Performance analysis of massively parallel embedded hardware architectures for retinal image processing. EURASIP J. Image Video Process. 10(1), 1–17 (2011).  https://doi.org/10.1186/1687-5281-2011-10 CrossRefGoogle Scholar
  63. 63.
    Page, D.: A Practical Introduction to Computer Architecture. Springer, Berlin (2009).  https://doi.org/10.1007/978-1-84882-256-6 CrossRefzbMATHGoogle Scholar
  64. 64.
    Pang, W.M., Choi, K.S., Qin, J.: Fast gabor texture feature extraction with separable filters using GPU. J. Real-Time Image Proc. 12(1), 5–13 (2016).  https://doi.org/10.1007/s11554-013-0373-y CrossRefGoogle Scholar
  65. 65.
    Rehman, T., Haber, E., Pryor, G., Melonakos, J., Tannenbaum, A.: 3D nonrigid registration via optimal mass transport on the GPU. Med. Image Anal. 13(6), 931–940 (2009).  https://doi.org/10.1016/j.media.2008.10.008 CrossRefGoogle Scholar
  66. 66.
    Riegler, M., Lux, M., Griwodz, C., Spampinato, C., De Lange, T., Eskeland, S., Pogorelov, K., Tavanapong, W., Schmidt, P., Gurrin, C., Johansen, D., Johansen, H., Halvorsen, P.: Multimedia and medicine: teammates for better disease detection and survival. Association for Computing Machinery, Inc, pp. 968–977 (2016)  https://doi.org/10.1145/2964284.2976760
  67. 67.
    Rodrigues, P., Bernardes, R.: 3-D adaptive nonlinear complex-diffusion despeckling filter. IEEE Trans. Med. Imaging 31(12), 2205–2212 (2012).  https://doi.org/10.1109/TMI.2012.2211609 CrossRefGoogle Scholar
  68. 68.
    Rohlfing, T., Maurer, J.C.R.: Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Trans. Inf Technol. Biomed. 7(1), 16–25 (2003).  https://doi.org/10.1109/TITB.2003.808506 CrossRefGoogle Scholar
  69. 69.
    Rohrer, J., Gong, L.: Accelerating 3D nonrigid registration using the cell broadband engine processor. IBM J. Res. Dev. 53(5), 1–10 (2009).  https://doi.org/10.1147/JRD.2009.5429078 CrossRefGoogle Scholar
  70. 70.
    Sabne, A., Wang, X., Kisner, S., Bouman, C., Raghunathan, A., Midkiff, S.: Model-based iterative CT image reconstruction on GPUs. In: Association for Computing Machinery, pp. 207–220 (2017)  https://doi.org/10.1145/3018743.3018765
  71. 71.
    Saiviroonporn, P., Robatino, A., Zahajszky, J., Kikinis, R., Jolesz, F.: Real-time interactive three-dimensional segmentation. Acad. Radiol. 5(1), 49–56 (1998).  https://doi.org/10.1016/S1076-6332(98)80011-1 CrossRefGoogle Scholar
  72. 72.
    Salomon, M., Heitz, F., Perrin, G.R., Armspach, J.P.: A massively parallel approach to deformable matching of 3D medical images via stochastic differential equations. Parallel Comput. 31(1), 45–71 (2005).  https://doi.org/10.1016/j.parco.2004.12.003 MathSciNetCrossRefGoogle Scholar
  73. 73.
    Samant, S., Xia, J., Muyan-Oelik, P., Owens, J.: High performance computing for deformable image registration: towards a new paradigm in adaptive radiotherapy. Med. Phys. 35(8), 3546–3553 (2008).  https://doi.org/10.1118/1.2948318 CrossRefGoogle Scholar
  74. 74.
    Saran, A.N., Nar, F., Saran, M.: Vessel segmentation in MRI using a variational image subtraction approach. J. Electr. Eng. Comput. Sci. 22(2), 499–516 (2014).  https://doi.org/10.3906/elk-1206-18 CrossRefGoogle Scholar
  75. 75.
    Schellmann, M., Gorlatch, S., Meilaender, D., Koesters, T., Schaefers, K., Wuebbeling, F., Burger, M.: Parallel medical image reconstruction: from graphics processing units (GPU) to grids. J. Supercomput. 57(2, SI), 151–160 (2011).  https://doi.org/10.1007/s11227-010-0397-z CrossRefGoogle Scholar
  76. 76.
    Schmid, J., Guitian, J.A.I., Gobbetti, E., Magnenat-Thalmann, N.: A GPU framework for parallel segmentation of volumetric images using discrete deformable models. Vis. Comput. 27(2, SI), 85–95 (2011).  https://doi.org/10.1007/s00371-010-0532-0 CrossRefGoogle Scholar
  77. 77.
    Sehellmann, M., Vörding, J., Gorlatch, S., Meiländer, D.: Cost-effective medical image reconstruction: from clusters to graphics processing units. In: Proceedings of the 5th Conference on Computing Frontiers, pp. 283–291 (2008).  https://doi.org/10.1145/1366230.1366278
  78. 78.
    Serrano, E., Blas, J., Carretero, J.: A comparative study of an X-ray tomography reconstruction algorithm in accelerated and cloud computing systems. Concurr Comput 27(18), 5538–5556 (2015).  https://doi.org/10.1002/cpe.3599 CrossRefGoogle Scholar
  79. 79.
    Shackleford, J.A., Kandasamy, N., Sharp, G.C.: On developing b-spline registration algorithms for multi-core processors. Phys. Med. Biol. 55(21), 6329–6351 (2010).  https://doi.org/10.1088/0031-9155/55/21/001 CrossRefGoogle Scholar
  80. 80.
    Shams, R., Sadeghi, P., Kennedy, R., Hartley, R.: Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical images. Comput. Methods Programs Biomed. 99(2), 133–146 (2010).  https://doi.org/10.1016/j.cmpb.2009.11.004 CrossRefGoogle Scholar
  81. 81.
    Shams, R., Sadeghi, P., Kennedy, R., Hartley, R.: A survey of medical image registration on multicore and the GPU. IEEE Signal Process. Mag. 27(2), 50–60 (2010).  https://doi.org/10.1109/MSP.2009.935387 CrossRefGoogle Scholar
  82. 82.
    Sharma, R., Sharma, A.: Segmentation methods in atherosclerosis vascular imaging. J. Inform. Med. Slov. 11, 52–69 (2006)Google Scholar
  83. 83.
    Shi, W., Li, Y., Miao, Y., Hu, Y.: Research on the key technology of image guided surgery. Prz. Elektrotech. 88(3B), 29–33 (2012)Google Scholar
  84. 84.
    Smistad, E., Bozorgi, M., Lindseth, F.: Fast: framework for heterogeneous medical image computing and visualization. Int. J. Comput. Assist. Radiol. Surg. 10(11), 1811–1822 (2015).  https://doi.org/10.1007/s11548-015-1158-5 CrossRefGoogle Scholar
  85. 85.
    Tan, G., Zhang, C., Wang, W., Zhang, P.: SuperDragon: a heterogeneous parallel system for accelerating 3D reconstruction of cryo-electron microscopy images. ACM Trans. Reconfig. Technol. Syst. 8(4), 1–22 (2015).  https://doi.org/10.1145/2851141.2851163 CrossRefGoogle Scholar
  86. 86.
    Tirado-Ramos, A., Sloot, P., Hoekstra, A., Bubak, M.: An integrative approach to high-performance biomedical problem solving environments on the grid. Parallel Comput. 30(9–10), 1037–1055 (2004).  https://doi.org/10.1016/j.parco.2004.07.010 CrossRefGoogle Scholar
  87. 87.
    Toennies, K.D.: Digital Image Acquisition, pp. 21–82. Springer, London (2012).  https://doi.org/10.1007/978-1-4471-2751-2_2 CrossRefGoogle Scholar
  88. 88.
    Treibig, J., Hager, G., Hofmann, H.G., Hornegger, J., Wellein, G.: Pushing the limits for medical image reconstruction on recent standard multicore processors. Int. J. High Perform. Comput. Appl. 27(2), 162–177 (2013).  https://doi.org/10.1177/1094342012442424 CrossRefGoogle Scholar
  89. 89.
    Ustun, T., Iftimia, N., Ferguson, R., Hammer, D.: Real-time processing for fourier domain optical coherence tomography using a field programmable gate array. Rev. Sci. Instrum. 79(11) (2008).  https://doi.org/10.1063/1.3005996 CrossRefGoogle Scholar
  90. 90.
    Vadja, A.: Programming Many-Core Chips. Springer, Berlin (2011).  https://doi.org/10.1007/978-1-4419-9739-5 CrossRefGoogle Scholar
  91. 91.
    Mei, W., Hwu, W. (eds.): GPU Computing GEMS - Emerald Edition. Morgan Kaufmann, Los Altos (2012)Google Scholar
  92. 92.
    Wachowiak, M., Peters, T.: High-performance medical image registration using new optimization techniques. IEEE Trans. Inf Technol. Biomed. 10(2), 344–353 (2006).  https://doi.org/10.1109/TITB.2006.864476 CrossRefGoogle Scholar
  93. 93.
    Wachowiak, M.P., Peters, T.M.: Parallel optimization approaches for medical image registration. Lect. Notes Comput. Sci. 3216, 781–788 (2004)CrossRefGoogle Scholar
  94. 94.
    Wang, X., Sabne, A., Kisner, S., Raghunathan, A., Bouman, C., Midkiff, S.: High performance model based image reconstruction. ACM 12(2), 1–12 (2016).  https://doi.org/10.1145/2851141.2851163 CrossRefGoogle Scholar
  95. 95.
    Warfield, S.K., Jolesz, F.A., Kikinis, R.: A high performance computing approach to the registration of medical imaging data. Parallel Comput. 24, 1345–1368 (1998).  https://doi.org/10.1016/S0167-8191(98)00061-1 CrossRefGoogle Scholar
  96. 96.
    Wei, Q., Patkar, S., Pai, D.K.: Fast ray-tracing of human eye optics on graphics processing units. Comput. Methods Programs Biomed. 114(3), 302–314 (2014).  https://doi.org/10.1016/j.cmpb.2014.02.003 CrossRefGoogle Scholar
  97. 97.
    Yeh, J.Y., Fu, J.: Parallel adaptive simulated annealing for computer-aided measurement in functional MRI analysis. Expert Syst. Appl. 33(3), 706–715 (2007).  https://doi.org/10.1016/j.eswa.2006.06.018 CrossRefGoogle Scholar
  98. 98.
    Yip, H., Ahmad, I., Pong, T.: An efficient parallel algorithm for computing the gaussian convolution of multi-dimensional image data. J. Supercomput. 14(3), 233–255 (1999).  https://doi.org/10.1023/A:1008137531862 CrossRefzbMATHGoogle Scholar
  99. 99.
    Zhu, Y.M., Cochoff, S.M.: Medical image viewing on multicore platforms using parallel computing patterns. IT Prof. 12(2), 33–41 (2010).  https://doi.org/10.1109/MITP.2010.62 CrossRefGoogle Scholar
  100. 100.
    Zhuge, Y., Cao, Y., Miller, R.W.: GPU accelerated fuzzy connected image segmentation by using CUDA. In: IEEE Engineering in Medicine and Biology Society, pp. 6341–6344 (2009).  https://doi.org/10.1109/IEMBS.2009.5333158
  101. 101.
    Zhuge, Y., Cao, Y., Udupa, J.K., Miller, R.W.: Parallel fuzzy connected image segmentation on GPU. Med. Phys. 38(7), 4365–4371 (2011).  https://doi.org/10.1118/1.3599725 CrossRefGoogle Scholar
  102. 102.
    Zhuge, Y., Ciesielski, K.C., Udupa, J.K., Miller, R.W.: GPU-based relative fuzzy connectedness image segmentation. Med. Phys. 40(1), 1–10 (2013).  https://doi.org/10.1118/1.4769418 CrossRefGoogle Scholar
  103. 103.
    Zinterhof, P.: High-throughput-screening of medical image data on heterogeneous clusters. Lecture Notes in Computer Science 7116 LNCS:368–377, (2012)  https://doi.org/10.1007/978-3-642-29843-1_42, cited By 0CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Carlos A. S. J. Gulo
    • 1
    • 2
  • Antonio C. Sementille
    • 3
  • João Manuel R. S. Tavares
    • 4
    Email author
  1. 1.CNPq National Scientific and Technological Development CouncilResearch Group PIXEL - UNEMATAlto Araguaia-MTBrazil
  2. 2.Programa Doutoral em Engenharia Informática, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  3. 3.Departamento de Ciências da Computação, Faculdade de CiênciasUniversidade Estadual Paulista-UNESPBauru-SPBrazil
  4. 4.Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de EngenhariaUniversidade do PortoPortoPortugal

Personalised recommendations