Improving 3D Medical Image Registration CUDA Software with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "William B. Langdon and Marc Modat and 
                 Justyna Petke and Mark Harman",
  title =        "Improving {3D} Medical Image Registration {CUDA}
                 Software with Genetic Programming",
  booktitle =    "GECCO '14: Proceeding of the sixteenth annual
                 conference on genetic and evolutionary computation
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  pages =        "951--958",
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Improvement, SBSE, GPGPU, Medicine, Software
                 engineering, Artificial Intelligence, Automatic
                 Programming, Software Engineering",
  month =        "12-15 " # jul,
  isbn13 =       "978-1-4503-2662-9",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1145/2576768.2598244",
  abstract =     "Genetic Improvement (GI) is shown to optimise, in some
                 cases by more than 35percent, a critical component of
                 healthcare industry software across a diverse range of
                 six nVidia graphics processing units (GPUs). GP and
                 other search based software engineering techniques can
                 automatically optimise the current rate limiting CUDA
                 parallel function in the NiftyReg open source C++
                 project used to align or register high resolution
                 nuclear magnetic resonance NMRI and other diagnostic
                 NIfTI images. Future Neurosurgery techniques will
                 require hardware acceleration, such as GPGPU, to enable
                 real time comparison of three dimensional in theatre
                 images with earlier patient images and reference data.
                 With millimetre resolution brain scan measurements
                 comprising more than ten million voxels the modified
                 kernel can process in excess of 3 billion active voxels
                 per second.",
  notes =        "

                 One page summary in \cite{Langdon:2014:ukmac}. Also
                 known as \cite{2598244} Cited by

                 GECCO-2014 A joint meeting of the twenty third
                 international conference on genetic algorithms
                 (ICGA-2014) and the nineteenth annual genetic
                 programming conference (GP-2014)",

Genetic Programming entries for William B Langdon Marc Modat Justyna Petke Mark Harman