Data exploration in evolutionary reconstruction of PET images

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

  author =       "Cameron C. Gray and Shatha F. Al-Maliki and 
                 Franck P. Vidal",
  title =        "Data exploration in evolutionary reconstruction of
                 {PET} images",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2018",
  volume =       "19",
  number =       "3",
  pages =        "391--419",
  month =        sep,
  note =         "Special issue on genetic programming, evolutionary
                 computation and visualization",
  keywords =     "genetic algorithms, genetic programming, Parisian
                 Approach, Fly Algorithm, Tomography reconstruction,
                 Information visualisation, Data exploration, Artificial
                 evolution, Parisian evolution",
  ISSN =         "1389-2576",
  URL =          "",
  DOI =          "doi:10.1007/s10710-018-9330-7",
  size =         "29 pages",
  abstract =     "This work is based on a cooperative co-evolution
                 algorithm called Fly Algorithm, which is an
                 evolutionary algorithm (EA) where individuals are
                 called flies. It is a specific case of the Parisian
                 Approach where the solution of an optimisation problem
                 is a set of individuals (e.g. the whole population)
                 instead of a single individual (the best one) as in
                 typical EAs. The optimisation problem considered here
                 is tomography reconstruction in positron emission
                 tomography (PET). It estimates the concentration of a
                 radioactive substance (called a radiotracer) within the
                 body. Tomography, in this context, is considered as a
                 difficult ill-posed inverse problem. The Fly Algorithm
                 aims at optimising the position of 3-D points that
                 mimic the radiotracer. At the end of the optimisation
                 process, the fly population is extracted as it
                 corresponds to an estimate of the radioactive
                 concentration. During the optimisation loop a lot of
                 data is generated by the algorithm, such as image
                 metrics, duration, and internal states. This data is
                 recorded in a log file that can be post-processed and
                 visualised. We propose using information visualisation
                 and user interaction techniques to explore the
                 algorithm's internal data. Our aim is to better
                 understand what happens during the evolutionary loop.
                 Using an example, we demonstrate that it is possible to
                 interactively discover when an early termination could
                 be triggered. It is implemented in a new stopping
                 criterion. It is tested on two other examples on which
                 it leads to a 60percent reduction of the number of
                 iterations without any loss of accuracy.",

Genetic Programming entries for Cameron C Gray Shatha F Al-Maliki Franck P Vidal