| dc.contributor.author | Péchaud, Mickaël | |
| dc.contributor.author | Peyré, Gabriel | |
| dc.contributor.author | Keriven, Renaud | |
| dc.date.accessioned | 2010-01-26T10:27:25Z | |
| dc.date.available | 2010-01-26T10:27:25Z | |
| dc.date.issued | 2008 | |
| dc.identifier.uri | https://basepub.dauphine.fr/handle/123456789/3138 | |
| dc.description | Version remaniée sur http://basepub.dauphine.fr/xmlui/handle/123456789/492 | |
| dc.language.iso | en | en |
| dc.subject | vessels segmentation | en |
| dc.subject | cortical imaging | en |
| dc.subject | retinal imaging | en |
| dc.subject | shortest path | en |
| dc.subject | fast marching | en |
| dc.subject | network | en |
| dc.subject.ddc | 621.3 | en |
| dc.title | Extraction of Vessels Networks over an Orientation Domain | en |
| dc.type | Document de travail / Working paper | |
| dc.description.abstracten | This paper presents a new method to extract a network of vessels centerlines from a medical image. The network is composed of local geodesics over a four-dimensional space that includes local orientation and scale. These shortest paths follow closely the center of vessels and can deal robustly with crossings over the image plane. The vessel network is grown by an iterative algorithm that distributes seed points according to a geodesic saliency field. Numerical experiments on a database of synthetic and medical images show the superiority of our approach with respect to several methods based on shortest paths extractions. With a minimum of user interaction, it allows to compute a complex network of vessels over noisy medical images. | en |
| dc.publisher.name | Université Paris-Dauphine | |
| dc.publisher.city | Paris | |
| dc.identifier.citationpages | 19 | en |
| dc.identifier.urlsite | http://hal.archives-ouvertes.fr/hal-00365021/en/ | en |
| dc.description.sponsorshipprivate | oui | en |
| dc.subject.ddclabel | Traitement du signal | en |
