title: Cross-scale coefficient selection for volumetric medical image fusion.
abstract: Joint analysis of medical data collected from different imaging modalities has become a common clinical practice. Therefore, image fusion techniques, which provide an efficient way of combining and enhancing information, have drawn increasing attention from the medical community. In this paper, we propose a novel cross-scale fusion rule for multiscale-decomposition-based fusion of volumetric medical images taking into account both intrascale and interscale consistencies. An optimal set of coefficients from the multiscale representations of the source images is determined by effective exploitation of neighborhood information. An efficient color fusion scheme is also proposed. Experiments demonstrate that our fusion rule generates better results than existing rules.
authors.affiliation: Department ofComputing Science, University of Alberta, Edmonton, AB, Canada. rshen@ualberta.ca
authors.forename: Rui
authors.initials: R
authors.lastname: Shen
authors.forename: Irene
authors.initials: I
authors.lastname: Cheng
authors.forename: Anup
authors.initials: A
authors.lastname: Basu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D016208:Databases, Factual; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D011859:Radiography; D015203:Reproducibility of Results; D058067:Wavelet Analysis