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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.


The hard segmentation of two different subjects.
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Related In: Results  -  Collection


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fig2: The hard segmentation of two different subjects.

Mentions: MR image intensities between subjects can be quite variable, even for the same pulse sequence and the same scanner. Differences may be caused by pulse sequence variations, intensity inhomogeneities, flow artifacts, scanner gain differences, and motion ghosting. This in turn can cause the hard segmentation of MR images to be very inconsistent between different scans. Inconsistencies between subjects are particularly evident on the brain cortex, as demonstrated in Figure 2. The highlighted sulci on these segmented images, which should be homologous and therefore registered to each other, are inconsistent in appearance; in particular, the CSF appears to be split in one subject but not the other. Because of this inconsistency, HAMMER's exclusive reliance on hard segmentations can cause registration errors. Mjolnir addresses this problem both by using histogram equalization and by incorporating the soft segmentations into the attribute vectors.


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

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

The hard segmentation of two different subjects.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: The hard segmentation of two different subjects.
Mentions: MR image intensities between subjects can be quite variable, even for the same pulse sequence and the same scanner. Differences may be caused by pulse sequence variations, intensity inhomogeneities, flow artifacts, scanner gain differences, and motion ghosting. This in turn can cause the hard segmentation of MR images to be very inconsistent between different scans. Inconsistencies between subjects are particularly evident on the brain cortex, as demonstrated in Figure 2. The highlighted sulci on these segmented images, which should be homologous and therefore registered to each other, are inconsistent in appearance; in particular, the CSF appears to be split in one subject but not the other. Because of this inconsistency, HAMMER's exclusive reliance on hard segmentations can cause registration errors. Mjolnir addresses this problem both by using histogram equalization and by incorporating the soft segmentations into the attribute vectors.

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.