A Comparison of Acceleration Techniques for Nonrigid Medical Image Registration
Mutual information based nonrigid registration of medical images is a popular approach. The coordinate mapping that relates the two images is found in an iterative optimisation procedure. In every iteration a computationally expensive evaluation of the mutual information’s derivative is required. In this work two acceleration strategies are compared. The first technique aims at reducing the number of iterations, and, consequently, the number of derivative evaluations. The second technique reduces the computational costs per iteration by employing stochastic approximations of the derivatives. The performance of both methods is tested on an artificial registration problem, where the ground truth is known, and on a clinical problem involving low-dose CT scans and large deformations. The experiments show that the stochastic approximation approach is superior in terms of speed and robustness. However, more accurate solutions are obtained with the first technique.
KeywordsMutual Information Gradient Descent Stochastic Approximation Nonrigid Registration Stochastic Gradient Descent
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