Al-Maliki, Shatha F.Lutton, ÉvelyneBoué, FrançoisVidal, Franck{Tam, Gary K. L. and Vidal, Franck2018-09-192018-09-192018978-3-03868-071-0https://diglib.eg.org:443/handle/10.2312/cgvc20181216https://doi.org/10.2312/cgvc.20181216In this study, we combine computer vision and visualisation/data exploration to analyse magnetic resonance imaging (MRI) data and detect garden peas inside the stomach. It is a preliminary objective of a larger project that aims to understand the kinetics of gastric emptying. We propose to perform the image analysis task as a multi-objective optimisation. A set of 7 equally important objectives are proposed to characterise peas. We rely on a cooperation co-evolution algorithm called 'Fly Algorithm' implemented using NSGA-II. The Fly Algorithm is a specific case of the 'Parisian Approach' where the solution of an optimisation problem is represented as a set of individuals (e.g. the whole population) instead of a single individual (the best one) as in typical evolutionary algorithms (EAs). NSGA-II is a popular EA used to solve multi-objective optimisation problems. The output of the optimisation is a succession of datasets that progressively approximate the Pareto front, which needs to be understood and explored by the end-user. Using interactive Information Visualisation (InfoVis) and clustering techniques, peas are then semi-automatically segmented.Humancentered computingVisualization application domainsComputing methodologiesSearch methodologiesGraphics systems and interfacesApplied computingLife and medical sciences10.2312/cgvc.20181216121-125