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

No Thumbnail Available
Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description

        
@inproceedings{
10.2312:cgvc.20181216
, booktitle = {
Computer Graphics and Visual Computing (CGVC)
}, editor = {
{Tam, Gary K. L. and Vidal, Franck
}, title = {{
Evolutionary Interactive Analysis of MRI Gastric Images Using a Multiobjective Cooperative-coevolution Scheme
}}, author = {
Al-Maliki, Shatha F.
 and
Lutton, Évelyne
 and
Boué, François
 and
Vidal, Franck
}, year = {
2018
}, publisher = {
The Eurographics Association
}, ISBN = {
978-3-03868-071-0
}, DOI = {
10.2312/cgvc.20181216
} }
Citation