Semisupervised Probabilistic Clustering of Brain MR Images Including Prior Clinical Information
Identifieur interne : 000C39 ( Istex/Corpus ); précédent : 000C38; suivant : 000C40Semisupervised Probabilistic Clustering of Brain MR Images Including Prior Clinical Information
Auteurs : Annemie Ribbens ; Frederik Maes ; Dirk Vandermeulen ; Paul SuetensSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: Accurate morphologic clustering of subjects and detection of population specific differences in brain MR images, due to e.g. neurological diseases, is of great interest in medical image analysis. In previous work, we proposed a probabilistic framework for unsupervised image clustering that allows exposing cluster specific morphological differences in each image. In this paper, we extend this framework to also accommodate semisupervised clustering approaches which provides the possibility of including prior knowledge about cluster memberships, group-level morphological differences and clinical prior knowledge. The method is validated on three different data sets and a comparative study between the supervised, semisupervised and unsupervised methods is performed. We show that the use of a limited amount of prior knowledge about cluster memberships can contribute to a better clustering performance in certain applications, while on the other hand the semisupervised clustering is quite robust to incorrect prior clustering knowledge.
Url:
DOI: 10.1007/978-3-642-18421-5_18
Links to Exploration step
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In previous work, we proposed a probabilistic framework for unsupervised image clustering that allows exposing cluster specific morphological differences in each image. In this paper, we extend this framework to also accommodate semisupervised clustering approaches which provides the possibility of including prior knowledge about cluster memberships, group-level morphological differences and clinical prior knowledge. The method is validated on three different data sets and a comparative study between the supervised, semisupervised and unsupervised methods is performed. We show that the use of a limited amount of prior knowledge about cluster memberships can contribute to a better clustering performance in certain applications, while on the other hand the semisupervised clustering is quite robust to incorrect prior clustering knowledge.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Annemie Ribbens</name><affiliations><json:string>Faculty of Engineering, Department of Electrical Engineering - ESAT, Center for Processing Speech and Images - PSI, Katholieke Universiteit Leuven, Belgium</json:string><json:string>E-mail: annemie.ribbens@uz.kuleuven.ac.be</json:string></affiliations></json:item><json:item><name>Frederik Maes</name><affiliations><json:string>Faculty of Engineering, Department of Electrical Engineering - ESAT, Center for Processing Speech and Images - PSI, Katholieke Universiteit Leuven, Belgium</json:string></affiliations></json:item><json:item><name>Dirk Vandermeulen</name><affiliations><json:string>Faculty of Engineering, Department of Electrical Engineering - ESAT, Center for Processing Speech and Images - PSI, Katholieke Universiteit Leuven, Belgium</json:string></affiliations></json:item><json:item><name>Paul Suetens</name><affiliations><json:string>Faculty of Engineering, Department of Electrical Engineering - ESAT, Center for Processing Speech and Images - PSI, Katholieke Universiteit Leuven, Belgium</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: Accurate morphologic clustering of subjects and detection of population specific differences in brain MR images, due to e.g. neurological diseases, is of great interest in medical image analysis. 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In previous work, we proposed a probabilistic framework for unsupervised image clustering that allows exposing cluster specific morphological differences in each image. In this paper, we extend this framework to also accommodate semisupervised clustering approaches which provides the possibility of including prior knowledge about cluster memberships, group-level morphological differences and clinical prior knowledge. The method is validated on three different data sets and a comparative study between the supervised, semisupervised and unsupervised methods is performed. 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University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><City>Zurich</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>Stanford</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-642-18421-5</BookID><BookTitle>Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging</BookTitle><BookSubTitle>International MICCAI Workshop, MCV 2010, Beijing, China, September 20, 2010, Revised Selected Papers</BookSubTitle><BookVolumeNumber>6533</BookVolumeNumber><BookSequenceNumber>6533</BookSequenceNumber><BookDOI>10.1007/978-3-642-18421-5</BookDOI><BookTitleID>216514</BookTitleID><BookPrintISBN>978-3-642-18420-8</BookPrintISBN><BookElectronicISBN>978-3-642-18421-5</BookElectronicISBN><BookChapterCount>21</BookChapterCount><BookCopyright><CopyrightHolderName>Springer Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I22021" Priority="1" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I21017" Priority="2" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Bjoern</GivenName><FamilyName>Menze</FamilyName></EditorName><Contact><Email>menze@csail.mit.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Georg</GivenName><FamilyName>Langs</FamilyName></EditorName><Contact><Email>langs@csail.mit.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Zhuowen</GivenName><FamilyName>Tu</FamilyName></EditorName><Contact><Email>zhuowen.tu@loni.ucla.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Antonio</GivenName><FamilyName>Criminisi</FamilyName></EditorName><Contact><Email>antcrim@microsoft.com</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Computer Science and Artificial Intelligence Laboratory</OrgDivision><OrgName>MIT</OrgName><OrgAddress><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>CSAIL - Computer Science and Artificial Intelligence Laboratory</OrgDivision><OrgName>MIT</OrgName><OrgAddress><Postcode>02139</Postcode><City>Cambridge</City><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Laboratory of Neuroimaging</OrgDivision><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgName>Machine Learning and Perception Group, Microsoft Research Cambridge</OrgName><OrgAddress><Country>UK</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part4"><PartInfo TocLevels="0"><PartID>4</PartID><PartSequenceNumber>4</PartSequenceNumber><PartTitle>Texture Analysis</PartTitle><PartChapterCount>4</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Medical Computer Vision</BookTitle></PartContext></PartInfo><Chapter ID="Chap18" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>18</ChapterID><ChapterDOI>10.1007/978-3-642-18421-5_18</ChapterDOI><ChapterSequenceNumber>18</ChapterSequenceNumber><ChapterTitle Language="En">Semisupervised Probabilistic Clustering of Brain MR Images Including Prior Clinical Information</ChapterTitle><ChapterFirstPage>184</ChapterFirstPage><ChapterLastPage>194</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>4</PartID><BookID>978-3-642-18421-5</BookID><BookTitle>Medical Computer Vision</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Annemie</GivenName><FamilyName>Ribbens</FamilyName></AuthorName><Contact><Email>annemie.ribbens@uz.kuleuven.ac.be</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Frederik</GivenName><FamilyName>Maes</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Dirk</GivenName><FamilyName>Vandermeulen</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Paul</GivenName><FamilyName>Suetens</FamilyName></AuthorName></Author><Affiliation ID="Aff20"><OrgDivision>Faculty of Engineering, Department of Electrical Engineering - ESAT, Center for Processing Speech and Images - PSI</OrgDivision><OrgName>Katholieke Universiteit Leuven</OrgName><OrgAddress><Country>Belgium</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Accurate morphologic clustering of subjects and detection of population specific differences in brain MR images, due to e.g. neurological diseases, is of great interest in medical image analysis. In previous work, we proposed a probabilistic framework for unsupervised image clustering that allows exposing cluster specific morphological differences in each image. In this paper, we extend this framework to also accommodate semisupervised clustering approaches which provides the possibility of including prior knowledge about cluster memberships, group-level morphological differences and clinical prior knowledge. The method is validated on three different data sets and a comparative study between the supervised, semisupervised and unsupervised methods is performed. We show that the use of a limited amount of prior knowledge about cluster memberships can contribute to a better clustering performance in certain applications, while on the other hand the semisupervised clustering is quite robust to incorrect prior clustering knowledge.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Semisupervised Probabilistic Clustering of Brain MR Images Including Prior Clinical Information</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Semisupervised Probabilistic Clustering of Brain MR Images Including Prior Clinical Information</title></titleInfo><name type="personal"><namePart type="given">Annemie</namePart><namePart type="family">Ribbens</namePart><affiliation>Faculty of Engineering, Department of Electrical Engineering - ESAT, Center for Processing Speech and Images - PSI, Katholieke Universiteit Leuven, Belgium</affiliation><affiliation>E-mail: annemie.ribbens@uz.kuleuven.ac.be</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Frederik</namePart><namePart type="family">Maes</namePart><affiliation>Faculty of Engineering, Department of Electrical Engineering - ESAT, Center for Processing Speech and Images - PSI, Katholieke Universiteit Leuven, Belgium</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Dirk</namePart><namePart type="family">Vandermeulen</namePart><affiliation>Faculty of Engineering, Department of Electrical Engineering - ESAT, Center for Processing Speech and Images - PSI, Katholieke Universiteit Leuven, Belgium</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Paul</namePart><namePart type="family">Suetens</namePart><affiliation>Faculty of Engineering, Department of Electrical Engineering - ESAT, Center for Processing Speech and Images - PSI, Katholieke Universiteit Leuven, Belgium</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2011</dateIssued><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Accurate morphologic clustering of subjects and detection of population specific differences in brain MR images, due to e.g. neurological diseases, is of great interest in medical image analysis. In previous work, we proposed a probabilistic framework for unsupervised image clustering that allows exposing cluster specific morphological differences in each image. In this paper, we extend this framework to also accommodate semisupervised clustering approaches which provides the possibility of including prior knowledge about cluster memberships, group-level morphological differences and clinical prior knowledge. The method is validated on three different data sets and a comparative study between the supervised, semisupervised and unsupervised methods is performed. We show that the use of a limited amount of prior knowledge about cluster memberships can contribute to a better clustering performance in certain applications, while on the other hand the semisupervised clustering is quite robust to incorrect prior clustering knowledge.</abstract><relatedItem type="host"><titleInfo><title>Medical Computer Vision. 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