Threshold Selection, Mitosis and Dual Mutation in Cooperative Co-evolution: Application to Medical 3D Tomography

  • Franck P. Vidal
  • Evelyne Lutton
  • Jean Louchet
  • Jean-Marie Rocchisani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6238)


We present and analyse the behaviour of specialised operators designed for cooperative coevolution strategy in the framework of 3D tomographic PET reconstruction. The basis is a simple cooperative co-evolution scheme (the “fly algorithm”), which embeds the searched solution in the whole population, letting each individual be only a part of the solution. An individual, or fly, is a 3D point that emits positrons. Using a cooperative co-evolution scheme to optimize the position of positrons, the population of flies evolves so that the data estimated from flies matches measured data. The final population approximates the radioactivity concentration. In this paper, three operators are proposed, threshold selection, mitosis and dual mutation, and their impact on the algorithm efficiency is experimentally analysed on a controlled test-case. Their extension to other cooperative co-evolution schemes is discussed.


Positron Emission Tomography Threshold Selection Mutation Variance Adaptive Mutation Initial Population Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Franck P. Vidal
    • 1
  • Evelyne Lutton
    • 2
  • Jean Louchet
    • 3
  • Jean-Marie Rocchisani
    • 4
  1. 1.Department of Radiation OncologyUniversity of CaliforniaSan Diego
  2. 2.AVIZ teamINRIA - Saclay-Île-de-FranceOrsayFrance
  3. 3.ArteniaChâtillonFrance
  4. 4.UFR SMBH & Avicenne hospitalParis XIII UniversityBobignyFrance

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