Optimized 2D/3D Medical Image Registration using the Estimation of Multivariate Normal Algorithm (EMNA)

X. Yang (Switzerland), W. Birkfellner (Austria), and P. Niederer (Switzerland)


2D-3D registration, optimization, estimation of evolution algorithm, medical image processing In this paper, we propose that the transformation variables are mutually dependent and prove that the t


Recently, a new type of iterative optimization algorithm Estimation of Distribution Algorithms (EDAs) was put forward [10]. The idea behind this approach is to use estimation and sampling to replace the crossover and mutation of Genetic Algorithms (GA). In each generation, the population (i.e. a collection of points) is replaced by a probability distribution that explicitly models that population. The covariance matrix of the search distribution reflects correlated parameters (non separability), and its application is especially suitable for ill conditioned and non-separable problems and for problems where the cost function exhibits some long narrow valleys. Dependencies of transformation parameters utilized in 2D/3D registration are firstly described, and a probabilistic distribution model of the transformation parameters is derived. Second, the Estimation of Multivariate Normal Algorithm (EMNA) is introduced for 2D/3D registration. A number of tests were performed using simulated data. The results demonstrate that EMNA is a potential powerful candidate for an optimized 2D/3D registration process.

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