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Mjolnir: extending HAMMER using a diffusion transformation model and histogram equalization for deformable image registration.

Ellingsen LM, Prince JL - Int J Biomed Imaging (2009)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA. lotta@jhu.edu

ABSTRACT
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.

No MeSH data available.


Average Dice coefficients for different anatomical regions on the left and right hemisphere (labeled L and R) for Mjolnir registration with and without histogram equalization in its preprocessing routine. The top set of bars shows the average over all regions and the error bars represent one standard deviation in each direction.
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fig7: Average Dice coefficients for different anatomical regions on the left and right hemisphere (labeled L and R) for Mjolnir registration with and without histogram equalization in its preprocessing routine. The top set of bars shows the average over all regions and the error bars represent one standard deviation in each direction.

Mentions: We ran Mjolnir with and without histogram equalization first to measure directly the benefits of including histogram equalization in the preprocessing routine. All other parameters were kept the same. We registered 15 MR brain images to a 16th randomly selected template image. The average Dice coefficient was computed to measure the overlap between corresponding regions of all subjects after registration. Results are shown in Figure 7.


Mjolnir: extending HAMMER using a diffusion transformation model and histogram equalization for deformable image registration.

Ellingsen LM, Prince JL - Int J Biomed Imaging (2009)

Average Dice coefficients for different anatomical regions on the left and right hemisphere (labeled L and R) for Mjolnir registration with and without histogram equalization in its preprocessing routine. The top set of bars shows the average over all regions and the error bars represent one standard deviation in each direction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2724857&req=5

fig7: Average Dice coefficients for different anatomical regions on the left and right hemisphere (labeled L and R) for Mjolnir registration with and without histogram equalization in its preprocessing routine. The top set of bars shows the average over all regions and the error bars represent one standard deviation in each direction.
Mentions: We ran Mjolnir with and without histogram equalization first to measure directly the benefits of including histogram equalization in the preprocessing routine. All other parameters were kept the same. We registered 15 MR brain images to a 16th randomly selected template image. The average Dice coefficient was computed to measure the overlap between corresponding regions of all subjects after registration. Results are shown in Figure 7.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA. lotta@jhu.edu

ABSTRACT
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.

No MeSH data available.