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Conference papers
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.
https://hal.archives-ouvertes.fr/hal-02266289
Contributor : Archive Ouverte Prodinra <>
Submitted on : Tuesday, August 13, 2019 - 8:07:16 PM
Last modification on : Tuesday, June 2, 2020 - 5:50:46 PM
Contributor : Archive Ouverte Prodinra <>
Submitted on : Tuesday, August 13, 2019 - 8:07:16 PM
Last modification on : Tuesday, June 2, 2020 - 5:50:46 PM