Details

Autor(en) / Beteiligte

• Vidal, Franck Patrick
• Louchet, Jean
• Rocchisani, Jean-Marie
• Lutton, Évelyne
• Preuß, Mike
• Goh, Chi-Keong
• Ekárt, Anikó
• Esparcia-Alcazar, Anna I.
• Ebner, Marc
• Neri, Ferrante
• Merelo, Juan J.
• Cotta, Carlos
• Togelius, Julian
• Yannakakis, Georgios N.
• Di Chio, Cecilia
• Cagnoni, Stefano

Titel

New Genetic Operators in the Fly Algorithm: Application to Medical PET Image Reconstruction

Ist Teil von

• Applications of Evolutionary Computation, p.292-301

Ort / Verlag

Berlin, Heidelberg: Springer Berlin Heidelberg

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• This paper presents an evolutionary approach for image reconstruction in positron emission tomography (PET). Our reconstruction method is based on a cooperative coevolution strategy (also called Parisian evolution): the “fly algorithm”. Each fly is a 3D point that mimics a positron emitter. The flies’ position is progressively optimised using evolutionary computing to closely match the data measured by the imaging system. The performance of each fly is assessed using a “marginal evaluation” based on the positive or negative contribution of this fly to the performance of the population. Using this property, we propose a “thresholded-selection” method to replace the classical tournament method. A mitosis operator is also proposed. It is triggered to automatically increase the population size when the number of flies with negative fitness becomes too low.