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


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.


Medical imaging Image segmentation Image registration Image reconstruction 



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).


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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

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