title: Self-similarity weighted mutual information: a new nonrigid image registration metric.
abstract: Extending mutual information (MI), which has been widely used as a similarity measure for rigid registration of multi-modal images, to deformable registration is an active field of research. We propose a self-similarity weighted graph-based implementation of alpha-mutual information (alpha-MI) for nonrigid image registration. The new Self Similarity alpha-MI (SeSaMI) metric takes local structures into account and is robust against signal non-stationarity and intensity distortions. We have used SeSaMI as the similarity measure in a regularized cost function with B-spline deformation field. Since the gradient of SeSaMI can be derived analytically, the cost function can be efficiently optimized using stochastic gradient descent. We show that SeSaMI produces a robust and smooth cost function and outperforms the state of the art statistical based similarity metrics in simulation and using data from image-guided neurosurgery.
authors.affiliation: Montreal Neurological Institute, McGill University.
authors.forename: Hassan
authors.initials: H
authors.lastname: Rivaz
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique