Evolutionary Interactive Analysis of MRI Gastric Images Using a Multiobjective Cooperative-coevolution Scheme

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dc.contributor.authorAl-Maliki, Shatha F.en_US
dc.contributor.authorLutton, Évelyneen_US
dc.contributor.authorBoué, Françoisen_US
dc.contributor.authorVidal, Francken_US
dc.contributor.editor{Tam, Gary K. L. and Vidal, Francken_US
dc.date.accessioned2018-09-19T15:15:19Z
dc.date.available2018-09-19T15:15:19Z
dc.date.issued2018
dc.description.abstractIn 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.en_US
dc.description.sectionheadersShort Papers
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20181216
dc.identifier.isbn978-3-03868-071-0
dc.identifier.pages121-125
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20181216
dc.identifier.urihttps://doi.org/10.2312/cgvc.20181216
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisualization application domains
dc.subjectComputing methodologies
dc.subjectSearch methodologies
dc.subjectGraphics systems and interfaces
dc.subjectApplied computing
dc.subjectLife and medical sciences
dc.titleEvolutionary Interactive Analysis of MRI Gastric Images Using a Multiobjective Cooperative-coevolution Schemeen_US
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