Fast semi-automatic segmentation based on reduced basis

Damiano Lombardi; Yvon Maday; Lydie Uro

doi:10.5802/crmath.89

Analyse numérique

Fast semi-automatic segmentation based on reduced basis
[Méthode de segmentation semi-automatique fondée sur une base réduite]

Damiano Lombardi ¹ ; Yvon Maday ² ; Lydie Uro ³

¹ COMMEDIA, Inria Paris, 2 rue Simone Iff, 75012, Paris, et Sorbonne Université, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France
² Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris et Institut Universitaire de France, France
³ Sorbonne Université, Institut des Sciences du Calcul et des Données (ISCD), F-75005 Paris, France

Comptes Rendus. Mathématique, Volume 358 (2020) no. 9-10, pp. 981-987.

Résumé
Abstract

Nous présentons ici une méthode de segmentation d’imagerie médicale semi-automatique minimisant l’intervention d’un expert. A l’aide d’une base réduite, nous connaissons a priori la forme de l’objet à identifier sur les images, comme un muscle sur un scanner. Il suffit d’identifier les coefficients associés à l’objet d’intérêt dans la base réduite, via la résolution d’un système linéaire prenant en entrée les coordonnées de quelques points sélectionnés sur l’image. Un exemple implémenté en 2D est proposé. Cette méthode est indépendante des niveaux de gris de l’image, et peut donc être appliquée sur tous objets et toutes imageries.

This note adresses the following segmentation problem in medical imaging: minimize expert intervention for semi-automatic segmentation process. Using a reduced basis, we have an a priori knowledge of the objet we want to identify on the images, like a muscle on a CT-Scan. We just have to identify the coefficients associated to the object of interest in the reduced basis, by solving a linear system taking as input the coordinates of some selected points in the image. An example implemented in 2D is shown. This method is independent of the grayscale of the image, and can therefore be applied to all objects and images.

Reçu le : 2019-10-11
Révisé le : 2020-05-26
Accepté le : 2020-06-24
Publié le : 2021-01-05

DOI : 10.5802/crmath.89

Affiliations des auteurs :

Damiano Lombardi ¹ ; Yvon Maday ² ; Lydie Uro ³

¹ COMMEDIA, Inria Paris, 2 rue Simone Iff, 75012, Paris, et Sorbonne Université, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France
² Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris et Institut Universitaire de France, France
³ Sorbonne Université, Institut des Sciences du Calcul et des Données (ISCD), F-75005 Paris, France

Licence :

CC-BY 4.0

Droits d'auteur : Les auteurs conservent leurs droits

@article{CRMATH_2020__358_9-10_981_0,
     author = {Damiano Lombardi and Yvon Maday and Lydie Uro},
     title = {Fast semi-automatic segmentation based on reduced basis},
     journal = {Comptes Rendus. Math\'ematique},
     pages = {981--987},
     publisher = {Acad\'emie des sciences, Paris},
     volume = {358},
     number = {9-10},
     year = {2020},
     doi = {10.5802/crmath.89},
     language = {en},
}

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Damiano Lombardi; Yvon Maday; Lydie Uro. Fast semi-automatic segmentation based on reduced basis. Comptes Rendus. Mathématique, Volume 358 (2020) no. 9-10, pp. 981-987. doi : 10.5802/crmath.89. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.89/

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