HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Zhu, Y.-M. Articles by Cochoff, S. M. Search for Related Content PubMed PubMed Citation Articles by Zhu, Y.-M. Articles by Cochoff, S. M.

Influence of Implementation Parameters on Registration of MR and SPECT Brain Images by Maximization of Mutual Information

¹ Nuclear Medicine Division, Philips Medical Systems, Cleveland, Ohio

Mutual-information maximization is one of the most popular algorithms for automatic image registration. However, many implementation issues have not been evaluated in a single, coherent context. Methods: Twenty-one registrations between MR and SPECT brain images (8 patients) were achieved by mutual-information maximization with different implementation strategies. The results of a popular strategy were chosen as the standard. All other results were compared with the standard, and the statistics of misregistrations were computed. The registration speed, accuracy, precision, and success rate were assessed. Results: Compared with trilinear interpolation, nearest-neighbor interpolation slightly sped the registration process, but with a lower success rate. The number of bins used to estimate the probability density function (pdf) affects the speed and robustness. Using fewer bins yielded a less robust registration. Adaptively changing the number of bins increased the registration speed and robustness. Simplex optimization increased the registration speed considerably, with a slightly degraded success rate. Simplex optimization with adaptive bin strategy improved the success rate and further decreased the registration time. Multiresolution optimization yielded a better success rate, with little effect on the accuracy and precision of registration. An increase in the number of resolution levels increased the success rate. Multisampling optimization also improved the success rate, but the results were less accurate and precise than those obtained with multiresolution optimization, with an increase in the number of levels decreasing the performance. Segmentation affected the registration speed and success rate. Because segmentation is problem specific, the effects were not conclusive. Conclusion: Different implementation strategies considerably affect the performance of automatic image registration by mutual-information maximization. On the basis of the experimental findings, we suggest that the best implementation strategy would include trilinear interpolation, adaptive change of the number of bins when estimating pdf, and exploitation of a simplex optimization algorithm with a multiresolution scheme.

Key Words: image registration • mutual information • MRI • SPECT