title: Multispectral image registration based on local canonical correlation analysis.
abstract: Medical scans are today routinely acquired using multiple sequences or contrast settings, resulting in multispectral data. For the automatic analysis of this data, the evaluation of multispectral similarity is essential. So far, few concepts have been proposed to deal in a principled way with images containing multiple channels. Here, we present a new approach based on a well known statistical technique: canonical correlation analysis (CCA). CCA finds a mapping of two multidimensional variables into two new bases, which best represent the true underlying relations of the signals. In contrast to previously used metrics, it is therefore able to find new correlations based on linear combinations of multiple channels. We extend this concept to efficiently model local canonical correlation (LCCA) between image patches. This novel, more general similarity metric can be applied to images with an arbitrary number of channels. The most important property of LCCA is its invariance to affine transformations of variables. When used on local histograms, LCCA can also deal with multimodal similarity. We demonstrate the performance of our concept on challenging clinical multispectral datasets.
authors.forename: Mattias P
authors.initials: MP
authors.lastname: Heinrich
authors.forename: Bartłomiej W
authors.initials: BW
authors.lastname: Papiez
authors.forename: Julia A
authors.initials: JA
authors.lastname: Schnabel
authors.forename: Heinz
authors.initials: H
authors.lastname: Handels
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000033:anatomy & histology*; D004285:Dogs; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D018482:Muscle, Skeletal / Q000033:anatomy & histology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D013382:Subtraction Technique