journal_papers.bib

@article{Gray2018GenetProgramEvolvableMach,
  title = {Data exploration in evolutionary reconstruction of {PET} images},
  journal = {Genetic Programming and Evolvable Machines},
  volume = 19,
  number = 3,
  pages = {391-419},
  year = 2018,
  month = sep,
  issn = {1573-7632},
  doi = {10.1007/s10710-018-9330-7},
  author = {Cameron C. Gray and Shatha F. {Al-Maliki} and Franck P. Vidal},
  keywords = {Fly Algorithm},
  keywords = {Tomography reconstruction},
  keywords = {Information visualisation},
  keywords = {Data exploration},
  keywords = {Artificial evolution},
  keywords = {Parisian evolution},
  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 60\% reduction of the number of iterations 
    without any loss of accuracy.},
  pdf = {./pdf/Gray2018GenetProgramEvolvableMach.pdf}
}

@article{Abbood2017SwarmEvolComput,
  title = {Voxelisation in the {3-D} {Fly} Algorithm for {PET}},
  journal = {Swarm and Evolutionary Computation},
  volume = 36,
  pages = {91-105},
  year = 2017,
  issn = {2210-6502},
  doi = {10.1016/j.swevo.2017.04.001},
  author = {Zainab Ali Abbood and Julien Lavauzelle and \'Evelyne Lutton and Jean-Marie Rocchisani and Jean Louchet and Franck P. Vidal},
  keywords = {Fly Algorithm},
  keywords = {Evolutionary computation},
  keywords = {Tomography},
  keywords = {Reconstruction algorithms},
  keywords = {Iterative algorithms},
  keywords = {Inverse problems},
  keywords = {Iterative reconstruction},
  keywords = {Co-operative co-evolution},
  abstract = {Abstract The Fly Algorithm was initially developed for 3-D robot vision applications. It consists in solving the inverse problem of shape reconstruction from projections by evolving a 
population of 3-D points in space (the ‘flies’), using an evolutionary optimisation strategy. Here, in its version dedicated to tomographic reconstruction in medical imaging, the flies are mimicking 
radioactive photon sources. Evolution is controlled using a fitness function based on the discrepancy of the projections simulated by the flies with the actual pattern received by the sensors. The 
reconstructed radioactive concentration is derived from the population of flies, i.e. a collection of points in the 3-D Euclidean space, after convergence. ‘Good’ flies were previously binned into 
voxels. In this paper, we study which flies to include in the final solution and how this information can be sampled to provide more accurate datasets in a reduced computation time. We investigate 
the use of density fields, based on Metaballs and on Gaussian functions respectively, to obtain a realistic output. The spread of each Gaussian kernel is modulated in function of the corresponding 
fly fitness. The resulting volumes are compared with previous work in terms of normalised-cross correlation. In our test-cases, data fidelity increases by more than 10% when density fields are used 
instead of binning. Our method also provides reconstructions comparable to those obtained using well-established techniques used in medicine (filtered back-projection and ordered subset 
expectation-maximisation).},
  pdf = {./pdf/AliAbbood2017SwarmEvolComput.pdf}
}

@article{Vidal2012IEEETransBiomedEng,
  author = {F. P. Vidal and {P.-F.} Villard and \'E. Lutton},
  title = {Tuning of Patient Specific Deformable Models using an Adaptive 
    Evolutionary Optimization Strategy},
  journal = {IEEE Transactions on Biomedical Engineering},
  year = 2012,
  volume = 59,
  pages = {2942-2949},
  number = 10,
  month = oct,
  abstract = {We present and analyze the behavior of an evolutionary algorithm
    designed to estimate the parameters of a complex organ behavior model.
    The model is adaptable to account for patients specificities. The aim is to
    finely tune the model to be accurately adapted to various real patient
    datasets. It can then be embedded, for example, in high fidelity simulations
    of the human physiology. We present here an application focused on
    respiration modeling. The algorithm is automatic and adaptive. A compound
    fitness function has been designed to take into account for various
    quantities that have to be minimized. The algorithm efficiency is
    experimentally analyzed on several real test-cases: i) three patient
    datasets have been acquired with the breath hold protocol, and ii) two
    datasets corresponds to 4D CT scans. Its performance is compared with two
    traditional methods (downhill simplex and conjugate gradient descent), a
    random search and a basic realvalued genetic algorithm. The results show
    that our evolutionary scheme provides more significantly stable and accurate
    results.},
  doi = {10.1109/TBME.2012.2213251},
  pmid = {22907958},
  keywords = {Evolutionary computation, inverse problems, medical simulation, 
	adaptive algorithm},
  publisher = {IEEE},
  pdf = {pdf/Vidal2012IEEETransBiomedEng.pdf}
}

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