Hybrid spline-based multimodal registration using local measures for joint entropy and mutual information.
We introduce a new hybrid approach for spline-based elastic registration of multimodal medical images. The approach uses point landmarks as well as intensity information based on local analytic measures for joint entropy and mutual information. The information-theoretic similarity measures are computationally efficient and can be optimized independently for each voxel. We have applied our approach to synthetic images, brain phantom images, as well as clinically relevant multimodal medical images. We also compared our measures with previous measures.
Biesdorf
Andreas
A
Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg.
Wörz
Stefan
S
Kaiser
Hans-Jürgen
HJ
Stippich
Christoph
C
Rohr
Karl
K
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D019277:Entropy; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / 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