Modélisation et segmentation d’images échographiques endovasculaires - Modelization and segmentation of intravascular ultrasound images

Modélisation et segmentation d’images échographiques endovasculaires

Modelization and segmentation of intravascular ultrasound images

Frédéric Guérault Philippe Delachartre  Gérard Finet  Isabelle E. Magnin 

SIMAG Développement, 13397 Marseille Cedex 20, France

CREATIS, UMR CNRS 5515, affilié à l’INSERM, 69621 Villeurbanne Cedex, France

Hôpital Cardiologique de Lyon, Laboratoire d'hémodynamique, BP Lyon Monchat, 69394 lyon cedex 03

Corresponding Author Email:
26 April 2000
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We present a new algorithm for the segmentation of intravascular ultrasound images. The model used to describe the endoluminal section of the artery takes into account the granular aspect of the texture and all the geometric deformations inherent in intravascular ultrasound images. The segmentation algorithm is developped in the framework of the statistical theory of estimation. It is adapted to the image model and allows to determine all the concentric contours of the artery section. The use of a specific image model limits the field of possible solutions, and allows to obtain a performant and reliable algorithm. We illustrate the performances of this algorithm on synthetic images and on real intravascular ultrasound images.


Nous présentons un nouvel algorithme pour la segmentation d’images échographiques endovasculaires. Le modèle introduit pour décrire la section endoluminale de l’artère prend en compte l’aspect granulaire des textures et l’ensemble des déformations géométriques inhérentes aux images échographiques endovasculaires. L’algorithme de segmentation est développé dans le cadre général de la théorie statistique de l’estimation. Il est adapté au modèle d’image et permet de déterminer sur les images échographiques endovasculaires les différents contours concentriques de la section artérielle. Le modèle utilisé permet de restreindre fortement le domaine des solutions admissibles, ce qui permet d'obtenir un algorithme performant et fiable. Nous illustrons les performances de cet algorithme sur des images synthétiques et sur des images ultrasonores endovasculaires réelles acquises chez l’homme.


Deformable modele, maximum likelihood, segmentation, ultrasound image, artifact

Mots clés

Modèle déformable, maximum de vraisemblance, segmentation, image ultrasonore, artefact

1. Introduction
2. Nature De l’image Échographique
3. Modélisation De I’image
4. Estimation Statistique De La Forme
5. Résultats Expérimentaux
6. Conclusion Et Perspectives

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