n-SIFT: n-dimensional scale invariant feature transform.
We propose the n-dimensional scale invariant feature transform (n-SIFT) method for extracting and matching salient features from scalar images of arbitrary dimensionality, and compare this method's performance to other related features. The proposed features extend the concepts used for 2-D scalar images in the computer vision SIFT technique for extracting and matching distinctive scale invariant features. We apply the features to images of arbitrary dimensionality through the use of hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. We analyze the performance of a fully automated multimodal medical image matching technique based on these features, and successfully apply the technique to determine accurate feature point correspondence between pairs of 3-D MRI images and dynamic 3D + time CT data.
Cheung
Warren
W
Bioinformatics Program, University of British Columbia at the Centre for Molecular Medicine and Therapeutics, Vancouver, BC V5Z 4H4, Canada. wcheung@cmmt.ubc.ca
Hamarneh
Ghassan
G
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D004285:Dogs; D006321:Heart / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016011:Normal Distribution; D011877:Radionuclide Imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*