title: Fast feature based multi slice to volume registration using phase congruency.
abstract: Slice to volume registration is very useful in many medical imaging applications, for example, fusing static high resolution three dimensional (3D) image volumes to dynamic two dimensional (2D) slice data for deriving motion information in 3D. Though information theoretic registration methods such as Mutual Information are usually robust, they are time intensive and typically require a high level of field-of-view correspondence between the source and target images. In single slice to volume registration scenarios, where such correspondence is limited, registration accuracy and robustness often deteriorate. In this paper, we present a novel registration method that maintains robustness and accuracy while significantly increasing registration speed. Our approach employs multiple slice (as opposed to single slice) to volume registration, which increases the amount of potential matching information while maintaining a small number of slices and hence facilitates the often necessary high speed dynamic image acquisition. Our proposed registration approach first extracts phase congruency information from the slices/volume using oriented 2D Gabor wavelets. Using local non maximum suppression, we then automatically obtain a robust and accurate set of feature points that are subsequently matched using an Iterative Closest Point (ICP) approach. Validation on BrainWeb simulated magnetic resonance imaging (MRI) data showed significant gains in speed ( approximately 40-fold increase) when compared to conventional Mutual Information based volumetric registration while maintaining comparable robustness and accuracy levels.
authors.affiliation: Electrical and Computer Engineering Department of the University of British Columbia, Vancouver V6T1Z4, Canada. rupind@ece.ubc.ca
authors.forename: Rupin
authors.initials: R
authors.lastname: Dalvi
authors.forename: Rafeef
authors.initials: R
authors.lastname: Abugharbieh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / 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