Paper Info
Title
Shape prior in Variational Region Growing
Abstract
In this paper, we propose two solutions to integrate shape prior in a segmentation process based on region growing. Our special region growing algorithm relies upon a variational framework which allows to easily take into account shape prior in the segmentation process. Region growing is described as an optimization process that aims to minimize some special energy combining intensity function and shape information. Two kinds of energy are proposed depending on the existence of a reference model or the possibility to assess some shape features at voxel level. We applied positively these two approaches in the context of life imaging in order to segment mice kidneys from small animal CT-images and lacuno-canicular network from experimental high resolution Synchrotron Radiation X-Ray Computed Tomography (SRμCT) images.
Year
DOI
Venue
2012
10.1109/IPTA.2012.6469571
Image Processing Theory, Tools and Applications
Keywords
Field
DocType
computerised tomography,image segmentation,kidney,medical image processing,optimisation,synchrotron radiation,SRμCT images,animal CT-images,energy combining intensity function,high resolution synchrotron radiation X-ray computed tomography images,lacuno-canicular network,life imaging,mice kidneys,optimization process,reference model,region growing algorithm,segmentation process,shape information,shape prior,variational framework,variational region growing,voxel level,Biomedical imaging,Image segmentation,Shape prior
Voxel,Computer vision,Scale-space segmentation,Reference model,Pattern recognition,Segmentation,Computer science,Image segmentation,Region growing,Computed tomography,Artificial intelligence,Synchrotron radiation
Conference
ISSN
ISBN
Citations 
2154-5111
978-1-4673-2585-1
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Chantal Revol-muller1325.73
Jean-loïc Rose282.19
Alexandra Pacureanu394.84
Françoise Peyrin45522.48
Christophe Odet59719.86
Revol-Muller, C.600.34