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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.


Four cross sections of the averages of 15 images registered to a 16th template image. The arrows highlight the most apparent regions of improvement in Mjolnir over HAMMER.
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fig12: Four cross sections of the averages of 15 images registered to a 16th template image. The arrows highlight the most apparent regions of improvement in Mjolnir over HAMMER.

Mentions: As in previous experiments, we registered 15 subjects to a randomly selected template, using all images in the NIREP Na0 database. Average and variance images were computed for both Mjolnir and HAMMER. Figure 12 shows a cross-sectional view of the two average images together with the template image. It can be observed that the average image generated by Mjolnir is sharper than that of HAMMER, particularly within the cortex. The average image variance over all voxels within the template's brain was 52.3 for HAMMER and 34.5 for Mjolnir (where the intensity range of the images was (0, 255)).


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

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

Four cross sections of the averages of 15 images registered to a 16th template image. The arrows highlight the most apparent regions of improvement in Mjolnir over HAMMER.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig12: Four cross sections of the averages of 15 images registered to a 16th template image. The arrows highlight the most apparent regions of improvement in Mjolnir over HAMMER.
Mentions: As in previous experiments, we registered 15 subjects to a randomly selected template, using all images in the NIREP Na0 database. Average and variance images were computed for both Mjolnir and HAMMER. Figure 12 shows a cross-sectional view of the two average images together with the template image. It can be observed that the average image generated by Mjolnir is sharper than that of HAMMER, particularly within the cortex. The average image variance over all voxels within the template's brain was 52.3 for HAMMER and 34.5 for Mjolnir (where the intensity range of the images was (0, 255)).

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.