Data exploration in evolutionary reconstruction of PET images

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Gray:2018:GPEM,

• 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 = "https://doi.org/10.1007/s10710-018-9330-7",
• 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

Citations