Paper Info
Title
Geometric deformable model driven by CoCRFs: application to optical coherence tomography.
Abstract
We present a geometric deformable model driven by dynamically updated probability fields. The shape is defined with the signed distance function, and the internal (smoothness) energy consists of a C1 continuity constraint, a shape prior, and a term that forces the zero-level of the shape distance function towards a connected form. The image probability fields are estimated by our collaborative Conditional Random Field (CoCRF), which is updated during the evolution in an active learning manner: it infers class posteriors in pixels or regions with feature ambiguities by assessing the joint appearance of neighboring sites and using the classification confidence. We apply our method to Optical Coherence Tomography fundus images for the segmentation of geographic atrophies in dry age-related macular degeneration of the human eye.
Year
DOI
Venue
2008
10.1007/978-3-540-85988-8_105
MICCAI
Keywords
Field
DocType
image probability field,c1continuity constraint,classification confidence,shape distance function,optical coherence tomography fundus,class posterior,dynamically updated probability field,geometric deformable model driven,active learning manner,signed distance function,collaborative conditional random field,distance function,active learning,conditional random field
Active contour model,Conditional random field,Human eye,Computer vision,Optical coherence tomography,Pattern recognition,Computer science,Signed distance function,Segmentation,Metric (mathematics),Pixel,Artificial intelligence
Conference
Volume
Issue
ISSN
11
Pt 1
0302-9743
Citations 
PageRank 
References 
2
0.42
19
Authors
5
Name
Order
Citations
PageRank
Gabriel Tsechpenakis116014.47
Brandon Lujan261.24
Oscar Martinez3365.42
Giovanni Gregori461.24
Philip J Rosenfeld530.80