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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 coefficient for different anatomical regions on the left and right hemisphere (labeled L and R) for HAMMER and Mjolnir. The top set of bars shows the average over all regions, and the error bars represent the standard deviation.
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fig10: Average Dice coefficient for different anatomical regions on the left and right hemisphere (labeled L and R) for HAMMER and Mjolnir. The top set of bars shows the average over all regions, and the error bars represent the standard deviation.

Mentions: We evaluated the two registration algorithms using the NIREP database by choosing one brain randomly as a template, registering the 15 remaining brains to this template and computing the average Dice coefficient, region-by-region and overall. The results are shown in Figure 10. It is observed that Mjolnir gives a better alignment in 28 out of 32 of the labeled cortical structures than does HAMMER and gives a higher average Dice coefficient across all regions of the brain (top row in Figure 10), measuring 0.6804 with an SD of 0.0617 while HAMMER's average Dice coefficient was 0.6416 with an SD of 0.0789. In a statistical analysis using a paired t-test this result is shown to be statistically significant with a P-value of 4.02 × 10−06.


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 coefficient for different anatomical regions on the left and right hemisphere (labeled L and R) for HAMMER and Mjolnir. The top set of bars shows the average over all regions, and the error bars represent the standard deviation.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2724857&req=5

fig10: Average Dice coefficient for different anatomical regions on the left and right hemisphere (labeled L and R) for HAMMER and Mjolnir. The top set of bars shows the average over all regions, and the error bars represent the standard deviation.
Mentions: We evaluated the two registration algorithms using the NIREP database by choosing one brain randomly as a template, registering the 15 remaining brains to this template and computing the average Dice coefficient, region-by-region and overall. The results are shown in Figure 10. It is observed that Mjolnir gives a better alignment in 28 out of 32 of the labeled cortical structures than does HAMMER and gives a higher average Dice coefficient across all regions of the brain (top row in Figure 10), measuring 0.6804 with an SD of 0.0617 while HAMMER's average Dice coefficient was 0.6416 with an SD of 0.0789. In a statistical analysis using a paired t-test this result is shown to be statistically significant with a P-value of 4.02 × 10−06.

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.