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Simultaneous Registration of Multiple Images: Similarity Metrics and Efficient Optimization
May 2013 (vol. 35 no. 5)
pp. 1221-1233
Christian Wachinger, Dept. of Neurology, Massachusetts Inst. of Technol., Cambridge, MA, USA
N. Navab, Dept. of Inf., Tech. Univ. Munchen, Garching, Germany
We address the alignment of a group of images with simultaneous registration. Therefore, we provide further insights into a recently introduced framework for multivariate similarity measures, referred to as accumulated pair-wise estimates (APE), and derive efficient optimization methods for it. More specifically, we show a strict mathematical deduction of APE from a maximum-likelihood framework and establish a connection to the congealing framework. This is only possible after an extension of the congealing framework with neighborhood information. Moreover, we address the increased computational complexity of simultaneous registration by deriving efficient gradient-based optimization strategies for APE: Gauss-Newton and the efficient second-order minimization (ESM). We present next to SSD the usage of intrinsically nonsquared similarity measures in this least squares optimization framework. The fundamental assumption of ESM, the approximation of the perfectly aligned moving image through the fixed image, limits its application to monomodal registration. We therefore incorporate recently proposed structural representations of images which allow us to perform multimodal registration with ESM. Finally, we evaluate the performance of the optimization strategies with respect to the similarity measures, leading to very good results for ESM. The extension to multimodal registration is in this context very interesting because it offers further possibilities for evaluations, due to publicly available datasets with ground-truth alignment.
Index Terms:
optimisation,computational complexity,gradient methods,image registration,least mean squares methods,maximum likelihood estimation,Newton method,publicly available datasets,simultaneous multiple image registration,similarity metrics,efficient optimization,multivariate similarity measures,accumulated pair-wise estimates,APE,maximum-likelihood framework,congealing framework,computational complexity,gradient-based optimization strategies,Gauss-Newton,efficient second-order minimization,ESM,SSD,least squares optimization framework,monomodal registration,multimodal registration,ground-truth alignment,Approximation methods,Optimization methods,Estimation,Joints,Density functional theory,Convergence,multimodal,Registration,groupwise,simultaneous,optimization,similarity measures
Citation:
Christian Wachinger, N. Navab, "Simultaneous Registration of Multiple Images: Similarity Metrics and Efficient Optimization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1221-1233, May 2013, doi:10.1109/TPAMI.2012.196
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