Intensity gradient based registration and fusion of multi-modal images.
OBJECTIVE
A particular problem in image registration arises for multi-modal images taken from different imaging devices and/or modalities. Starting in 1995, mutual information has shown to be a very successful distance measure for multi-modal image registration. Therefore, mutual information is considered to be the state-of-the-art approach to multi-modal image registration. However, mutual information has also a number of well-known drawbacks. Its main disadvantage is that it is known to be highly non-convex and has typically many local maxima.


METHODS
This observation motivates us to seek a different image similarity measure which is better suited for optimization but as well capable to handle multi-modal images.


RESULTS
In this work, we investigate an alternative distance measure which is based on normalized gradients.


CONCLUSIONS
As we show, the alternative approach is deterministic, much simpler, easier to interpret, fast and straightforward to implement, faster to compute, and also much more suitable to numerical optimization.
Haber
Eldad
E
Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30233, USA. haber@mathcs.emory.edu
Modersitzki
Jan
J
D000465:Algorithms; D003952:Diagnostic Imaging; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D014481:United States