Using Spanning Graphs for Efficient Image Registration

• Published in: IEEE transactions on image processing Vol. 17; no. 5; pp. 788 - 797
• Main Authors: Sabuncu, M.R., Ramadge, P.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Alignment; Analogies; Applied sciences; Artificial Intelligence; Entropy; estimation; Estimators; Exact sciences and technology; Graphs; Image analysis; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image registration; Information, signal and communications theory; Mutual information; Optimization methods; Pattern Recognition, Automated - methods; Performance analysis; Reproducibility of Results; Sensitivity and Specificity; Signal processing; Subtraction Technique; Telecommunications and information theory; Time measurement; Transformations; Uncertainty; Yield estimation; estimation; Prior information; Image registration; Minimal graph; Algorithm performance; Similarity; Image processing; Entropy; Graph theory; Algorithm
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2008.918951

We provide a detailed analysis of the use of minimal spanning graphs as an alignment method for registering multimodal images. This yields an efficient graph theoretic algorithm that, for the first time, jointly estimates both an alignment measure and a viable descent direction with respect to a parameterized class of spatial transformations. We also show how prior information about the interimage modality relationship from prealigned image pairs can be incorporated into the graph-based algorithm. A comparison of the graph theoretic alignment measure is provided with more traditional measures based on plug-in entropy estimators. This highlights previously unrecognized similarities between these two registration methods. Our analysis gives additional insight into the tradeoffs the graph-based algorithm is making and how these will manifest themselves in the registration algorithm's performance.