title: Multimodality image registration using an extensible information metric and high dimensional histogramming.
abstract: We extend an information metric from intermodality (2-image) registration to multimodality (multiple-image) registration so that we can simultaneously register multiple images of different modalities. And we also provide the normalized version of the extensible information metric, which has better performance in high noise situations. Compared to mutual information which can even become negative in the multiple image case, our metric can be easily and naturally extended to multiple images. After using a new technique to efficiently compute high dimensional histograms, the extensible information metric can be efficiently computed even for multiple images. To showcase the new measure, we compare the results of direct multimodality registration using high-dimensional histogramming with repeated intermodality registration. We find that registering 3 images simultaneously with the new metric is more accurate than pair-wise registration on 2D images obtained from synthetic magnetic resonance (MR) proton density (PD), MR T2 and MR T1 3D volumes from Brain Web. We perform the unbiased registration of 5 multimodality images of anatomy, CT, MR PD, T1 and T2 from Visible Human Male Data with the normalized metric and high-dimensional histogramming. Our results demonstrate the efficacy of the metrics and high-dimensional histogramming in affine, multimodality image registration.
authors.affiliation: Dept. of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611-6120, USA. jiezhang@cise.ufl.edu
authors.forename: Jie
authors.initials: J
authors.lastname: Zhang
authors.forename: Anand
authors.initials: A
authors.lastname: Rangarajan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / 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; D014057:Tomography, X-Ray Computed / Q000379:methods*