A Framework for Automatic Tuning of System Parameters and Its Use in Image Registration

摘要

The performance of most segmentation and registration algorithms depends on the values of internal parameters. Most often, these are set empirically. This is a trial-and-error process in which the developer modifies the values in an attempt to improve performance. This is an implicit form of optimization. In this paper, we present a more intuitive and systematic framework for this type of problem. We then use it to estimate optimal parameter values of a common registration problem. We formulate the performance of the registration problem as a function of its internal parameters, and use optimization techniques to search for an optimal value for these parameters. Registration quality is evaluated using a set of training images in which the anatomy of interest was segmented and comparing the overlap between the segmentations as induced by the registration. As a large number of computationally complex registrations are performed during the optimization, a cluster of MPI-enabled computers are used collaboratively to reduce the computation time. We evaluated the proposed method using ten CT images of the liver from five patients, and evaluated three optimization algorithms. The results showed that, compared with the empirical values suggested in the published literature, our technique was able to obtain parameter values that are tuned for particular applications in a more intuitive and systematic way. In addition, the proposed framework can potentially be used to tune system parameter values appropriate for specific input types.