A unified statistical and information theoretic framework for multi-modal image registration.
We formulate and interpret several registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the auto-information function, as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the auto-information as well as verify them empirically on multi-modal imagery. Among the useful aspects of the auto-information function is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.
Zöllei
Lilla
L
Massachusetts Institute of Technology, Artificial Intelligence Laboratory, Cambridge, MA 02139, USA. lzollei@ai.mit.edu
Fisher
John W
JW
Wells
William M
WM
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D007257:Information Theory; D016013:Likelihood Functions; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011859:Radiography; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique