Evolutionary interactive analysis of MRI gastric images using a multiobjective cooperative-coevolution Ssheme

Abstract : In 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.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-02266289
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Submitted on : Tuesday, August 13, 2019 - 8:07:16 PM
Last modification on : Thursday, August 15, 2019 - 1:10:16 AM

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Shatha F. Al-Maliki, Evelyne Lutton, François Boué, Franck Vidal. Evolutionary interactive analysis of MRI gastric images using a multiobjective cooperative-coevolution Ssheme. Computer Graphics and Visual Computing (CGVC), 2018, Swansea, United Kingdom. ⟨10.2312/cgvc.20181216⟩. ⟨hal-02266289⟩

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