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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.


Example of an outlier occurring in HAMMER, where HAMMER's ability to follow the folding pattern of the cortex is lacking. The top row shows the deformed MR images and the corresponding template image on the right. The bottom row shows the corresponding deformed labels from the NIREP Na0 database and the template's labels.
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fig14: Example of an outlier occurring in HAMMER, where HAMMER's ability to follow the folding pattern of the cortex is lacking. The top row shows the deformed MR images and the corresponding template image on the right. The bottom row shows the corresponding deformed labels from the NIREP Na0 database and the template's labels.

Mentions: Figure 13 shows the sum of deformed mask points for both algorithms in the first two columns and the original template gray matter mask on the right for comparison. Visually, it is clear that Mjolnir yields fewer gross errors—that is, outliers—than does HAMMER. Figure 14 shows an example of the (labeled) gray matter mapping results from both HAMMER and Mjolnir, both in comparison to the true template labels. This example demonstrates a rather large alignment error in the HAMMER result, while the Mjolnir result is overall more accurate. While Mjolnir is not perfect, the more compact GM mask alignment revealed in Figure 13 together with the improvements in average Dice coefficient shows that these types of gross errors are less common in Mjolnir than in HAMMER. Some evidences of tissue shearing are visible in the results of Mjolnir shown in Figure 14. The fact is that dramatic deformations must sometimes take place in order to best align homologous brain structures. This is particularly true when aligning different brains. Most of Mjolnir's deformation fields are fairly smooth; however, when a dramatic change is required in order to align important landmarks, it has the flexibility to do so. Figure 14 is an example of such a case.


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

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

Example of an outlier occurring in HAMMER, where HAMMER's ability to follow the folding pattern of the cortex is lacking. The top row shows the deformed MR images and the corresponding template image on the right. The bottom row shows the corresponding deformed labels from the NIREP Na0 database and the template's labels.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2724857&req=5

fig14: Example of an outlier occurring in HAMMER, where HAMMER's ability to follow the folding pattern of the cortex is lacking. The top row shows the deformed MR images and the corresponding template image on the right. The bottom row shows the corresponding deformed labels from the NIREP Na0 database and the template's labels.
Mentions: Figure 13 shows the sum of deformed mask points for both algorithms in the first two columns and the original template gray matter mask on the right for comparison. Visually, it is clear that Mjolnir yields fewer gross errors—that is, outliers—than does HAMMER. Figure 14 shows an example of the (labeled) gray matter mapping results from both HAMMER and Mjolnir, both in comparison to the true template labels. This example demonstrates a rather large alignment error in the HAMMER result, while the Mjolnir result is overall more accurate. While Mjolnir is not perfect, the more compact GM mask alignment revealed in Figure 13 together with the improvements in average Dice coefficient shows that these types of gross errors are less common in Mjolnir than in HAMMER. Some evidences of tissue shearing are visible in the results of Mjolnir shown in Figure 14. The fact is that dramatic deformations must sometimes take place in order to best align homologous brain structures. This is particularly true when aligning different brains. Most of Mjolnir's deformation fields are fairly smooth; however, when a dramatic change is required in order to align important landmarks, it has the flexibility to do so. Figure 14 is an example of such a case.

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.