Evolutionary interactive analysis of MRI gastric images using a multiobjective cooperative-coevolution Ssheme
| العنوان: | Evolutionary interactive analysis of MRI gastric images using a multiobjective cooperative-coevolution Ssheme |
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| المؤلفون: | Al-Maliki, Shatha F., Lutton, Évelyne, Boué, François, Vidal, Franck P. |
| المساهمون: | Bangor University, Génie et Microbiologie des Procédés Alimentaires (GMPA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Laboratoire Léon Brillouin (LLB - UMR 12), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), UMR 12 Laboratoire Léon Brillouin (LLB), Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay, AgroParisTech-Institut National de la Recherche Agronomique (INRA) |
| المصدر: | Computer Graphics and Visual Computing (CGVC) Computer Graphics and Visual Computing (CGVC), 2018, Swansea, United Kingdom. ⟨10.2312/cgvc.20181216⟩ |
| بيانات النشر: | HAL CCSD, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Graphics systems and interfaces, Visualization application domains, [SDV]Life Sciences [q-bio], Applied computing, medical imaging, multi-objective optimisation, centered computing, Computing methodologies, computer vision, Search methodologies, Life and medical sciences, visualisation, cooperative co-evolution, Human, IRM |
| الوصف: | 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. Computer Graphics and Visual Computing (CGVC) Short Papers 121 125 Shatha F. Al-Maliki, Évelyne Lutton, François Boué, and Franck P. Vidal CCS Concepts: Human-centered computing --> Visualization application domains;Computing methodologies --> Search methodologies; Graphics systems and interfaces;Applied computing --> Life and medical sciences |
| اللغة: | English |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d5fd481af592681537ef192d6a50dd1Test https://hal.science/hal-02266289Test |
| رقم الانضمام: | edsair.doi.dedup.....3d5fd481af592681537ef192d6a50dd1 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |