title: Random walks with efficient search and contextually adapted image similarity for deformable registration.
abstract: We develop a random walk-based image registration method 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.
authors.affiliation: Medical Image Analysis Lab., Simon Fraser University, Burnaby, Canada. lisat@sfu.ca
authors.forename: Lisa Y W
authors.initials: LY
authors.lastname: Tang
authors.affiliation: Medical Image Analysis Lab., Simon Fraser University, Burnaby, Canada.
authors.forename: Ghassan
authors.initials: G
authors.lastname: Hamarneh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique