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Optimizing a medical image registration algorithm based on profiling data for real-time performance

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Abstract

Image registration is a commonly task in medical image analysis. Therefore, a significant number of algorithms have been developed to perform rigid and non-rigid image registration. Particularly, the free-form deformation algorithm is frequently used to carry out non-rigid registration task; however, it is a computationally very intensive algorithm. In this work, we describe an approach based on profiling data to identify potential parts of this algorithm for which parallel implementations can be developed. The proposed approach assesses the efficient of the algorithm by applying performance analysis techniques commonly available in traditional computer operating systems. Hence, this article provides guidelines to support researchers working on medical image processing and analysis to achieve real-time non-rigid image registration applications using common computing systems. According to our experimental findings, significant speedups can be accomplished by parallelizing sequential snippets, i.e., code regions that are executed more than once. For the selected costly functions previously identified in the studied free-form deformation algorithm, the developed parallelization decreased the runtime by up to seven times relatively to the related single thread based implementation. The implementations were developed based on the Open Multi-Processing application programming interface. In conclusion, this study confirms that based on the call graph visualization and detected performance bottlenecks, one can easily find and evaluate snippets which are potential optimization targets in addition to throughput in memory accesses.

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Notes

  1. gprof2dot is an open source script written in Python used to convert the output from a range of profiles into a dot graph. This script can be freely downloaded at https://github.com/jrfonseca/gprof2dot.

  2. dot is a Graphviz feature for producing hierarchical drawings of directed graphs. Graphviz is an open source visualization software for representing structural information such as diagrams of abstract graphs. More information is available at http://graphviz.org.

  3. An executable version of the FFD algorithm used for comparison purpose, by performing quantitative analysis based on the DSC value, can be downloaded from Daniel Rueckert’s webpage: http://www.doc.ic.ac.uk/~dr

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Acknowledgements

The first author gratefully acknowledges the following institutions for the support received: Universidade do Estado de Mato Grosso (UNEMAT), in Brazil, and National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq), process grant 234306/2014-9 under reference #2010/15691-0.

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Authors and Affiliations

  1. CNPq National Scientific and Technological Development Council, Research Group PIXEL - UNEMAT, Brasilia, Brazil

    Carlos A. S. J. Gulo

  2. Programa Doutoral em Engenharia Informática, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    Carlos A. S. J. Gulo

  3. Departamento de Ciências da Computação, Faculdade de Ciências, Universidade Estadual Paulista-UNESP, Prudente, Brazil

    Antonio C. Sementille

  4. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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Correspondence to João Manuel R. S. Tavares.

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Gulo, C.A.S.J., Sementille, A.C. & Tavares, J.M.R.S. Optimizing a medical image registration algorithm based on profiling data for real-time performance. Multimed Tools Appl 81, 2603–2620 (2022). https://doi.org/10.1007/s11042-021-11699-x

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