Paper Info
Title
Random Walks with Efficient Search and Contextually Adapted Image Similarity for Deformable Registration.
Abstract
We develop a random walk-based image registration method [1] that incorporates two novelties: 1) a progressive optimization scheme that conducts the solution search efficiently via a novel use of information derived from the obtained probabilistic solution, and 2) a data-likelihood re-weighting step that contextually performs feature selection in a spatially adaptive manner so that the data costs are based primarily on trusted information sources. Synthetic experiments on three public datasets of different anatomical regions and modalities showed that our method performed efficient search without sacrificing registration accuracy. Experiments performed on 60 real brain image pairs from a public dataset also demonstrated our method's better performance over existing non-probabilistic image registration methods.
Year
DOI
Venue
2013
10.1007/978-3-642-40763-5_6
Lecture Notes in Computer Science
Field
DocType
Volume
Modalities,Computer vision,Discretization error,Pattern recognition,Feature selection,Random walk,Computer science,Artificial intelligence,Probabilistic logic,Image registration
Conference
8150
Issue
ISSN
Citations 
Pt 2
0302-9743
11
PageRank 
References 
Authors
0.59
14
2
Name
Order
Citations
PageRank
Lisa Y. W. Tang11107.05
Ghassan Hamarneh21353110.14