MRI Gastric images processing using a multiobjective fly algorithm

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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Submitted on : Tuesday, August 13, 2019 - 8:07:39 PM
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  • HAL Id : hal-02266292, version 1
  • PRODINRA : 480192


Shatha Al-Maliki, Evelyne Lutton, François Boue, Franck Vidal. MRI Gastric images processing using a multiobjective fly algorithm. International Conference on Parallel Problem Solving from Nature, PPSN 2018, Sep 2018, Coimbra, Portugal. ⟨hal-02266292⟩



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