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International Journal of Biomedical Imaging
Volume 2009, Article ID 281615, 18 pages
Research Article

Mjolnir: Extending HAMMER Using a Diffusion Transformation Model and Histogram Equalization for Deformable Image Registration

Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA

Received 2 October 2008; Revised 19 March 2009; Accepted 24 April 2009

Academic Editor: Sun Yoo

Copyright © 2009 Lotta M. Ellingsen and Jerry L. Prince. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. A new approach to volumetric intersubject deformable image registration is presented. The method, called Mjolnir, is an extension of the highly successful method HAMMER. New image features in order to better localize points of correspondence between the two images are introduced as well as a novel approach to generate a dense displacement field based upon the weighted diffusion of automatically derived feature correspondences. An extensive validation of the algorithm was performed on T1-weighted SPGR MR brain images from the NIREP evaluation database. The results were compared with results generated by HAMMER and are shown to yield significant improvements in cortical alignment as well as reduced computation time.