A Markov random field approach for topology-preserving registration: application to object-based tomographic image interpolation.
Identifieur interne : 000317 ( PubMed/Corpus ); précédent : 000316; suivant : 000318A Markov random field approach for topology-preserving registration: application to object-based tomographic image interpolation.
Auteurs : Lucilio Cordero-Grande ; Gonzalo Vegas-Sánchez-Ferrero ; Pablo Casaseca-De-La-Higuera ; Carlos Alberola-L PezSource :
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society [ 1941-0042 ] ; 2012.
English descriptors
- KwdEn :
- Algorithms, Humans, Markov Chains, Pattern Recognition, Automated (methods), Radiographic Image Enhancement (methods), Radiographic Image Interpretation, Computer-Assisted (methods), Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Tomography, X-Ray Computed (methods).
- MESH :
Abstract
This paper proposes a topology-preserving multiresolution elastic registration method based on a discrete Markov random field of deformations and a block-matching procedure. The method is applied to the object-based interpolation of tomographic slices. For that purpose, the fidelity of a given deformation to the data is established by a block-matching strategy based on intensity- and gradient-related features, the smoothness of the transformation is favored by an appropriate prior on the field, and the deformation is guaranteed to maintain the topology by imposing some hard constraints on the local configurations of the field. The resulting deformation is defined as the maximum a posteriori configuration. Additionally, the relative influence of the fidelity and smoothness terms is weighted by the unsupervised estimation of the field parameters. In order to obtain an unbiased interpolation result, the registration is performed both in the forward and backward directions, and the resulting transformations are combined by using the local information content of the deformation. The method is applied to magnetic resonance and computed tomography acquisitions of the brain and the torso. Quantitative comparisons offer an overall improvement in performance with respect to related works in the literature. Additionally, the application of the interpolation method to cardiac magnetic resonance images has shown that the removal of any of the main components of the algorithm results in a decrease in performance which has proven to be statistically significant.
DOI: 10.1109/TIP.2011.2171354
PubMed: 21997265
Links to Exploration step
pubmed:21997265Le document en format XML
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The method is applied to the object-based interpolation of tomographic slices. For that purpose, the fidelity of a given deformation to the data is established by a block-matching strategy based on intensity- and gradient-related features, the smoothness of the transformation is favored by an appropriate prior on the field, and the deformation is guaranteed to maintain the topology by imposing some hard constraints on the local configurations of the field. The resulting deformation is defined as the maximum a posteriori configuration. Additionally, the relative influence of the fidelity and smoothness terms is weighted by the unsupervised estimation of the field parameters. In order to obtain an unbiased interpolation result, the registration is performed both in the forward and backward directions, and the resulting transformations are combined by using the local information content of the deformation. The method is applied to magnetic resonance and computed tomography acquisitions of the brain and the torso. Quantitative comparisons offer an overall improvement in performance with respect to related works in the literature. Additionally, the application of the interpolation method to cardiac magnetic resonance images has shown that the removal of any of the main components of the algorithm results in a decrease in performance which has proven to be statistically significant.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">21997265</PMID><DateCreated><Year>2012</Year><Month>03</Month><Day>22</Day></DateCreated><DateCompleted><Year>2012</Year><Month>07</Month><Day>18</Day></DateCompleted><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1941-0042</ISSN><JournalIssue CitedMedium="Internet"><Volume>21</Volume><Issue>4</Issue><PubDate><Year>2012</Year><Month>Apr</Month></PubDate></JournalIssue><Title>IEEE transactions on image processing : a publication of the IEEE Signal Processing Society</Title><ISOAbbreviation>IEEE Trans Image Process</ISOAbbreviation></Journal><ArticleTitle>A Markov random field approach for topology-preserving registration: application to object-based tomographic image interpolation.</ArticleTitle><Pagination><MedlinePgn>2047-61</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1109/TIP.2011.2171354</ELocationID><Abstract><AbstractText>This paper proposes a topology-preserving multiresolution elastic registration method based on a discrete Markov random field of deformations and a block-matching procedure. The method is applied to the object-based interpolation of tomographic slices. For that purpose, the fidelity of a given deformation to the data is established by a block-matching strategy based on intensity- and gradient-related features, the smoothness of the transformation is favored by an appropriate prior on the field, and the deformation is guaranteed to maintain the topology by imposing some hard constraints on the local configurations of the field. The resulting deformation is defined as the maximum a posteriori configuration. Additionally, the relative influence of the fidelity and smoothness terms is weighted by the unsupervised estimation of the field parameters. In order to obtain an unbiased interpolation result, the registration is performed both in the forward and backward directions, and the resulting transformations are combined by using the local information content of the deformation. The method is applied to magnetic resonance and computed tomography acquisitions of the brain and the torso. Quantitative comparisons offer an overall improvement in performance with respect to related works in the literature. Additionally, the application of the interpolation method to cardiac magnetic resonance images has shown that the removal of any of the main components of the algorithm results in a decrease in performance which has proven to be statistically significant.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Cordero-Grande</LastName><ForeName>Lucilio</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Teoría de la Señal y Comunicaciones e Ingeniería Telemática, University of Valladolid, Valladolid, Spain. lcorgra@lpi.tel.uva.es</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Vegas-Sánchez-Ferrero</LastName><ForeName>Gonzalo</ForeName><Initials>G</Initials></Author><Author ValidYN="Y"><LastName>Casaseca-de-la-Higuera</LastName><ForeName>Pablo</ForeName><Initials>P</Initials></Author><Author ValidYN="Y"><LastName>Alberola-López</LastName><ForeName>Carlos</ForeName><Initials>C</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2011</Year><Month>10</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>IEEE Trans Image Process</MedlineTA><NlmUniqueID>9886191</NlmUniqueID><ISSNLinking>1057-7149</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008390">Markov Chains</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010363">Pattern Recognition, Automated</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D011856">Radiographic Image Enhancement</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D011857">Radiographic Image Interpretation, Computer-Assisted</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D015203">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012680">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D013382">Subtraction Technique</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014057">Tomography, X-Ray Computed</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="aheadofprint"><Year>2011</Year><Month>10</Month><Day>13</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2011</Year><Month>10</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2011</Year><Month>10</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2012</Year><Month>7</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1109/TIP.2011.2171354</ArticleId><ArticleId IdType="pubmed">21997265</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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