HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation

Robust Adaptive Segmentation of 3D Medical Images with Level Sets

Caroline Baillard 1 Christian Barillot 1 Patrick Bouthemy 1
1 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : This paper is concerned with the use of the Level Set formalism to segment anatomical structures in 3D medical images (ultrasound or magnetic resonance images). A closed 3D surface propagates towards the desired boundaries through the iterative evolution of a 4D implicit function. The major contribution of this work is the design of a robust evolution model based on adaptive parameters depending on the data. First the step size and the external propagation force factor, both usually predetermined constants, are automatically computed at each iteration. Additionally, region-based information, rather than spatial image gradient, is exploited by estimating intensity probability density functions over the image. As a result, the method can be applied to various kinds of data. Quantitative and qualitative results on brain MR images and 3D echographies of carotid arteries are reported and discussed.
Document type :
Complete list of metadata

Cited literature [37 references]  Display  Hide  Download

Contributor : Rapport de Recherche Inria Connect in order to contact the contributor
Submitted on : Wednesday, May 24, 2006 - 10:17:35 AM
Last modification on : Friday, February 4, 2022 - 3:22:28 AM
Long-term archiving on: : Sunday, April 4, 2010 - 11:13:27 PM


  • HAL Id : inria-00072562, version 1


Caroline Baillard, Christian Barillot, Patrick Bouthemy. Robust Adaptive Segmentation of 3D Medical Images with Level Sets. [Research Report] RR-4071, INRIA. 2000. ⟨inria-00072562⟩



Record views


Files downloads