Computing global minimizers to a constrained B-spline image registration problem from optimal l1 perturbations to block match data.
Saved in:
| Title: | Computing global minimizers to a constrained B-spline image registration problem from optimal l |
|---|---|
| Authors: | Castillo, Edward1, Castillo, Richard2, Fuentes, David3, Guerrero, Thomas4 |
| Source: | Medical Physics. Apr2014, Vol. 41 Issue 4, p1-N.PAG. 11p. |
| Subjects: | Image registration, Perturbation theory, Statistical matching, Mathematical optimization, Voxel-based morphometry, Robust control |
| Abstract: | Purpose: Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization. Methods: The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimal l1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching. Results: The performance of the method is measured in terms of spatial accuracy using ten inhale/ exhale thoracic CT image pairs (available for download at www.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set. Conclusions: The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match generated estimates. Thus, the framework allows for a wide range of image similarity block match metric and physical modeling combinations. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 95464276 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Computing global minimizers to a constrained B-spline image registration problem from optimal l<subscript>1</subscript> perturbations to block match data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Castillo%2C+Edward%22">Castillo, Edward</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Castillo%2C+Richard%22">Castillo, Richard</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Fuentes%2C+David%22">Fuentes, David</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Guerrero%2C+Thomas%22">Guerrero, Thomas</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Apr2014, Vol. 41 Issue 4, p1-N.PAG. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Perturbation+theory%22">Perturbation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+matching%22">Statistical matching</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Voxel-based+morphometry%22">Voxel-based morphometry</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization. Methods: The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimal l1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching. Results: The performance of the method is measured in terms of spatial accuracy using ten inhale/ exhale thoracic CT image pairs (available for download at www.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set. Conclusions: The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match generated estimates. Thus, the framework allows for a wide range of image similarity block match metric and physical modeling combinations. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=95464276 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1118/1.4866891 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Image registration Type: general – SubjectFull: Perturbation theory Type: general – SubjectFull: Statistical matching Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Voxel-based morphometry Type: general – SubjectFull: Robust control Type: general Titles: – TitleFull: Computing global minimizers to a constrained B-spline image registration problem from optimal l1 perturbations to block match data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Castillo, Edward – PersonEntity: Name: NameFull: Castillo, Richard – PersonEntity: Name: NameFull: Fuentes, David – PersonEntity: Name: NameFull: Guerrero, Thomas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 41 – Type: issue Value: 4 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
