Decision Support System for the Diagnosis of Parkinson’s Disease
Identifieur interne : 000659 ( Main/Corpus ); précédent : 000658; suivant : 000660Decision Support System for the Diagnosis of Parkinson’s Disease
Auteurs : Anders Ericsson ; Nowak Lonsdale ; Kalle Astrom ; Lars Edenbrandt ; Lars FribergSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: Recently new nuclear medical diagnostic imaging methods have been introduced by which it is possible to diagnose Parkinson’s disease, PD, at an early stage. The binding of a specific receptor ligand [123I]-FP-CIT in specific structures of the human brain is visualized by way of single photon emission computerized tomography, SPECT. The interpretation of the SPECT data can be accessed by visual analysis and manual quantification methods. We have developed a computer aided automatic decision support system in attempt to facilitate the diagnosis of Parkinson’s disease from the acquired SPECT images. A rigid model was used for the segmentation of the basal ganglia of the human brain. The aim of the study was to develop an automated method for quantification and classification of the images. The study comprises SPECT scans 89 patients, who underwent a [123I]-FP-CIT examination because of suspected Parkinson’s disease. An experienced nuclear medicine physician interpreted the images and diagnosed 65 of the patients as most likely suffering from Parkinson’s disease. The method included the following steps; (i) segmentation of basal ganglia by fitting a rigid 3D model to the image, (ii) extraction of 17 features based on image intensity distribution in the basal ganglia and a reference based on image intensity distribution outside the basal ganglia, (iii) classification using Support Vector Machine (SVM). The classification based on the automated method showed a true acceptance of 96.9% and a true rejection of 91.6%. The classification based on a manual quantification method gave a true acceptance of 98.5% and a true rejection of 100%. The method proposed here is fully automatic and it makes use of the full 3D data set in contrast to a method that is widely used at hospitals today which only uses a few 2D image slices.
Url:
DOI: 10.1007/11499145_75
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Decision Support System for the Diagnosis of Parkinson’s Disease</title><author><name sortKey="Ericsson, Anders" sort="Ericsson, Anders" uniqKey="Ericsson A" first="Anders" last="Ericsson">Anders Ericsson</name></author><author><name sortKey="Lonsdale, Nowak" sort="Lonsdale, Nowak" uniqKey="Lonsdale N" first="Nowak" last="Lonsdale">Nowak Lonsdale</name></author><author><name sortKey="Astrom, Kalle" sort="Astrom, Kalle" uniqKey="Astrom K" first="Kalle" last="Astrom">Kalle Astrom</name></author><author><name sortKey="Edenbrandt, Lars" sort="Edenbrandt, Lars" uniqKey="Edenbrandt L" first="Lars" last="Edenbrandt">Lars Edenbrandt</name></author><author><name sortKey="Friberg, Lars" sort="Friberg, Lars" uniqKey="Friberg L" first="Lars" last="Friberg">Lars Friberg</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:FA06E1ADF42CC20B5A68B250AE72252E95EAFEBC</idno><date when="2005" year="2005">2005</date><idno type="doi">10.1007/11499145_75</idno><idno type="url">https://api.istex.fr/document/FA06E1ADF42CC20B5A68B250AE72252E95EAFEBC/fulltext/pdf</idno><idno type="wicri:Area/Main/Corpus">000659</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Decision Support System for the Diagnosis of Parkinson’s Disease</title><author><name sortKey="Ericsson, Anders" sort="Ericsson, Anders" uniqKey="Ericsson A" first="Anders" last="Ericsson">Anders Ericsson</name></author><author><name sortKey="Lonsdale, Nowak" sort="Lonsdale, Nowak" uniqKey="Lonsdale N" first="Nowak" last="Lonsdale">Nowak Lonsdale</name></author><author><name sortKey="Astrom, Kalle" sort="Astrom, Kalle" uniqKey="Astrom K" first="Kalle" last="Astrom">Kalle Astrom</name></author><author><name sortKey="Edenbrandt, Lars" sort="Edenbrandt, Lars" uniqKey="Edenbrandt L" first="Lars" last="Edenbrandt">Lars Edenbrandt</name></author><author><name sortKey="Friberg, Lars" sort="Friberg, Lars" uniqKey="Friberg L" first="Lars" last="Friberg">Lars Friberg</name></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2005</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">FA06E1ADF42CC20B5A68B250AE72252E95EAFEBC</idno><idno type="DOI">10.1007/11499145_75</idno><idno type="ChapterID">Chap75</idno><idno type="ChapterID">75</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Recently new nuclear medical diagnostic imaging methods have been introduced by which it is possible to diagnose Parkinson’s disease, PD, at an early stage. The binding of a specific receptor ligand [123I]-FP-CIT in specific structures of the human brain is visualized by way of single photon emission computerized tomography, SPECT. The interpretation of the SPECT data can be accessed by visual analysis and manual quantification methods. We have developed a computer aided automatic decision support system in attempt to facilitate the diagnosis of Parkinson’s disease from the acquired SPECT images. A rigid model was used for the segmentation of the basal ganglia of the human brain. The aim of the study was to develop an automated method for quantification and classification of the images. The study comprises SPECT scans 89 patients, who underwent a [123I]-FP-CIT examination because of suspected Parkinson’s disease. An experienced nuclear medicine physician interpreted the images and diagnosed 65 of the patients as most likely suffering from Parkinson’s disease. The method included the following steps; (i) segmentation of basal ganglia by fitting a rigid 3D model to the image, (ii) extraction of 17 features based on image intensity distribution in the basal ganglia and a reference based on image intensity distribution outside the basal ganglia, (iii) classification using Support Vector Machine (SVM). The classification based on the automated method showed a true acceptance of 96.9% and a true rejection of 91.6%. The classification based on a manual quantification method gave a true acceptance of 98.5% and a true rejection of 100%. The method proposed here is fully automatic and it makes use of the full 3D data set in contrast to a method that is widely used at hospitals today which only uses a few 2D image slices.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Anders Ericsson</name></json:item><json:item><name>Markus Nowak Lonsdale</name></json:item><json:item><name>Kalle Astrom</name></json:item><json:item><name>Lars Edenbrandt</name></json:item><json:item><name>Lars Friberg</name></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: Recently new nuclear medical diagnostic imaging methods have been introduced by which it is possible to diagnose Parkinson’s disease, PD, at an early stage. The binding of a specific receptor ligand [123I]-FP-CIT in specific structures of the human brain is visualized by way of single photon emission computerized tomography, SPECT. The interpretation of the SPECT data can be accessed by visual analysis and manual quantification methods. We have developed a computer aided automatic decision support system in attempt to facilitate the diagnosis of Parkinson’s disease from the acquired SPECT images. A rigid model was used for the segmentation of the basal ganglia of the human brain. The aim of the study was to develop an automated method for quantification and classification of the images. The study comprises SPECT scans 89 patients, who underwent a [123I]-FP-CIT examination because of suspected Parkinson’s disease. An experienced nuclear medicine physician interpreted the images and diagnosed 65 of the patients as most likely suffering from Parkinson’s disease. The method included the following steps; (i) segmentation of basal ganglia by fitting a rigid 3D model to the image, (ii) extraction of 17 features based on image intensity distribution in the basal ganglia and a reference based on image intensity distribution outside the basal ganglia, (iii) classification using Support Vector Machine (SVM). The classification based on the automated method showed a true acceptance of 96.9% and a true rejection of 91.6%. The classification based on a manual quantification method gave a true acceptance of 98.5% and a true rejection of 100%. 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The binding of a specific receptor ligand [123I]-FP-CIT in specific structures of the human brain is visualized by way of single photon emission computerized tomography, SPECT. The interpretation of the SPECT data can be accessed by visual analysis and manual quantification methods. We have developed a computer aided automatic decision support system in attempt to facilitate the diagnosis of Parkinson’s disease from the acquired SPECT images. A rigid model was used for the segmentation of the basal ganglia of the human brain. The aim of the study was to develop an automated method for quantification and classification of the images. The study comprises SPECT scans 89 patients, who underwent a [123I]-FP-CIT examination because of suspected Parkinson’s disease. An experienced nuclear medicine physician interpreted the images and diagnosed 65 of the patients as most likely suffering from Parkinson’s disease. The method included the following steps; (i) segmentation of basal ganglia by fitting a rigid 3D model to the image, (ii) extraction of 17 features based on image intensity distribution in the basal ganglia and a reference based on image intensity distribution outside the basal ganglia, (iii) classification using Support Vector Machine (SVM). The classification based on the automated method showed a true acceptance of 96.9% and a true rejection of 91.6%. The classification based on a manual quantification method gave a true acceptance of 98.5% and a true rejection of 100%. The method proposed here is fully automatic and it makes use of the full 3D data set in contrast to a method that is widely used at hospitals today which only uses a few 2D image slices.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I22021</label><label>I2203X</label><label>I22013</label><item><term>Computer Science</term></item><item><term>Image Processing and Computer Vision</term></item><item><term>Pattern Recognition</term></item><item><term>Computer Graphics</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2005">Published</change><change xml:id="refBibs-istex" who="#ISTEX-API" when="2016-3-1">References 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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><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>CA</City><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><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><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>New York University</OrgName><OrgAddress><City>NY</City><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>Saarbruecken</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>978-3-540-31566-7</BookID><BookTitle>Image Analysis</BookTitle><BookSubTitle>14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19-22, 2005. Proceedings</BookSubTitle><BookVolumeNumber>3540</BookVolumeNumber><BookSequenceNumber>3540</BookSequenceNumber><BookDOI>10.1007/b137285</BookDOI><BookTitleID>124798</BookTitleID><BookPrintISBN>978-3-540-26320-3</BookPrintISBN><BookElectronicISBN>978-3-540-31566-7</BookElectronicISBN><BookChapterCount>127</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</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="I2203X" Priority="2" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I22013" Priority="3" Type="Secondary">Computer Graphics</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Heikki</GivenName><FamilyName>Kalviainen</FamilyName></EditorName><Contact><Email>heikki.kalviainen@lut.fi</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Jussi</GivenName><FamilyName>Parkkinen</FamilyName></EditorName><Contact><Email>jussi.parkkinen@cs.joensuu.fi</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Arto</GivenName><FamilyName>Kaarna</FamilyName></EditorName><Contact><Email>arto.kaarna@lut.fi</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Department of Information Technology</OrgDivision><OrgName>Lappeenranta University of Technology</OrgName><OrgAddress><Postbox>P.O.Box 20</Postbox><Postcode>FIN-53851</Postcode><City>Lappeenranta</City><Country>Finland</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Dept. of Computer Science</OrgDivision><OrgName>University of Joensuu</OrgName><OrgAddress><Country>Finland</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Department of Information and Computer Sciences</OrgDivision><OrgName>Toyohashi University of Technology</OrgName><OrgAddress><Street>1-1 Hibarigaoka, Tenpaku-cho</Street><Postcode>441-8580</Postcode><City>Toyohashi</City><Country>Japan</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part14"><PartInfo TocLevels="0"><PartID>14</PartID><PartSequenceNumber>14</PartSequenceNumber><PartTitle>Poster Presentations 1: Image Analysis, Computer Vision, Machine Vision, and Applications</PartTitle><PartChapterCount>45</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Image Analysis</BookTitle></PartContext></PartInfo><Chapter ID="Chap75" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>75</ChapterID><ChapterDOI>10.1007/11499145_75</ChapterDOI><ChapterSequenceNumber>75</ChapterSequenceNumber><ChapterTitle Language="En">Decision Support System for the Diagnosis of Parkinson’s Disease</ChapterTitle><ChapterFirstPage>740</ChapterFirstPage><ChapterLastPage>749</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</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>14</PartID><BookID>978-3-540-31566-7</BookID><BookTitle>Image Analysis</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author><AuthorName DisplayOrder="Western"><GivenName>Anders</GivenName><FamilyName>Ericsson</FamilyName></AuthorName></Author><Author><AuthorName DisplayOrder="Western"><GivenName>Markus</GivenName><GivenName>Nowak</GivenName><FamilyName>Lonsdale</FamilyName></AuthorName></Author><Author><AuthorName DisplayOrder="Western"><GivenName>Kalle</GivenName><FamilyName>Astrom</FamilyName></AuthorName></Author><Author><AuthorName DisplayOrder="Western"><GivenName>Lars</GivenName><FamilyName>Edenbrandt</FamilyName></AuthorName></Author><Author><AuthorName DisplayOrder="Western"><GivenName>Lars</GivenName><FamilyName>Friberg</FamilyName></AuthorName></Author></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Recently new nuclear medical diagnostic imaging methods have been introduced by which it is possible to diagnose Parkinson’s disease, PD, at an early stage. The binding of a specific receptor ligand [123I]-FP-CIT in specific structures of the human brain is visualized by way of single photon emission computerized tomography, SPECT. The interpretation of the SPECT data can be accessed by visual analysis and manual quantification methods. We have developed a computer aided automatic decision support system in attempt to facilitate the diagnosis of Parkinson’s disease from the acquired SPECT images. A rigid model was used for the segmentation of the basal ganglia of the human brain. The aim of the study was to develop an automated method for quantification and classification of the images.</Para><Para>The study comprises SPECT scans 89 patients, who underwent a [123I]-FP-CIT examination because of suspected Parkinson’s disease. An experienced nuclear medicine physician interpreted the images and diagnosed 65 of the patients as most likely suffering from Parkinson’s disease.</Para><Para>The method included the following steps; (i) segmentation of basal ganglia by fitting a rigid 3D model to the image, (ii) extraction of 17 features based on image intensity distribution in the basal ganglia and a reference based on image intensity distribution outside the basal ganglia, (iii) classification using Support Vector Machine (SVM).</Para><Para>The classification based on the automated method showed a true acceptance of 96.9% and a true rejection of 91.6%. The classification based on a manual quantification method gave a true acceptance of 98.5% and a true rejection of 100%. The method proposed here is fully automatic and it makes use of the full 3D data set in contrast to a method that is widely used at hospitals today which only uses a few 2D image slices.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Decision Support System for the Diagnosis of Parkinson’s Disease</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Decision Support System for the Diagnosis of Parkinson’s Disease</title></titleInfo><name type="personal"><namePart type="given">Anders</namePart><namePart type="family">Ericsson</namePart><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Markus</namePart><namePart type="given">Nowak</namePart><namePart type="family">Lonsdale</namePart><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Kalle</namePart><namePart type="family">Astrom</namePart><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Lars</namePart><namePart type="family">Edenbrandt</namePart><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Lars</namePart><namePart type="family">Friberg</namePart><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [research-article]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2005</dateIssued><copyrightDate encoding="w3cdtf">2005</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: Recently new nuclear medical diagnostic imaging methods have been introduced by which it is possible to diagnose Parkinson’s disease, PD, at an early stage. The binding of a specific receptor ligand [123I]-FP-CIT in specific structures of the human brain is visualized by way of single photon emission computerized tomography, SPECT. The interpretation of the SPECT data can be accessed by visual analysis and manual quantification methods. We have developed a computer aided automatic decision support system in attempt to facilitate the diagnosis of Parkinson’s disease from the acquired SPECT images. A rigid model was used for the segmentation of the basal ganglia of the human brain. The aim of the study was to develop an automated method for quantification and classification of the images. The study comprises SPECT scans 89 patients, who underwent a [123I]-FP-CIT examination because of suspected Parkinson’s disease. An experienced nuclear medicine physician interpreted the images and diagnosed 65 of the patients as most likely suffering from Parkinson’s disease. The method included the following steps; (i) segmentation of basal ganglia by fitting a rigid 3D model to the image, (ii) extraction of 17 features based on image intensity distribution in the basal ganglia and a reference based on image intensity distribution outside the basal ganglia, (iii) classification using Support Vector Machine (SVM). The classification based on the automated method showed a true acceptance of 96.9% and a true rejection of 91.6%. The classification based on a manual quantification method gave a true acceptance of 98.5% and a true rejection of 100%. The method proposed here is fully automatic and it makes use of the full 3D data set in contrast to a method that is widely used at hospitals today which only uses a few 2D image slices.</abstract><relatedItem type="host"><titleInfo><title>Image Analysis</title><subTitle>14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19-22, 2005. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Heikki</namePart><namePart type="family">Kalviainen</namePart><affiliation>Department of Information Technology, Lappeenranta University of Technology, P.O.Box 20, FIN-53851, Lappeenranta, Finland</affiliation><affiliation>E-mail: heikki.kalviainen@lut.fi</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jussi</namePart><namePart type="family">Parkkinen</namePart><affiliation>Dept. of Computer Science, University of Joensuu, Finland</affiliation><affiliation>E-mail: jussi.parkkinen@cs.joensuu.fi</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Arto</namePart><namePart type="family">Kaarna</namePart><affiliation>Department of Information and Computer Sciences, Toyohashi University of Technology, 1-1 Hibarigaoka, Tenpaku-cho, 441-8580, Toyohashi, Japan</affiliation><affiliation>E-mail: arto.kaarna@lut.fi</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2005</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I22013">Computer Graphics</topic></subject><identifier type="DOI">10.1007/b137285</identifier><identifier type="ISBN">978-3-540-26320-3</identifier><identifier type="eISBN">978-3-540-31566-7</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">124798</identifier><identifier type="BookID">978-3-540-31566-7</identifier><identifier type="BookChapterCount">127</identifier><identifier type="BookVolumeNumber">3540</identifier><identifier type="BookSequenceNumber">3540</identifier><identifier type="PartChapterCount">45</identifier><part><date>2005</date><detail type="part"><title>Poster Presentations 1: Image Analysis, Computer Vision, Machine Vision, and Applications</title></detail><detail type="volume"><number>3540</number><caption>vol.</caption></detail><extent unit="pages"><start>740</start><end>749</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2005</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>New York University, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Dough</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><affiliation>Rice University, Houston, TX, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck Institute of Computer Science, Saarbruecken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2005</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2005</recordOrigin></recordInfo></relatedItem><identifier type="istex">FA06E1ADF42CC20B5A68B250AE72252E95EAFEBC</identifier><identifier type="DOI">10.1007/11499145_75</identifier><identifier type="ChapterID">Chap75</identifier><identifier type="ChapterID">75</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg</accessCondition><recordInfo><recordContentSource>SPRINGER</recordContentSource><recordOrigin>Springer-Verlag Berlin Heidelberg, 2005</recordOrigin></recordInfo></mods></metadata><enrichments><istex:refBibTEI uri="https://api.istex.fr/document/FA06E1ADF42CC20B5A68B250AE72252E95EAFEBC/enrichments/refBib"><teiHeader></teiHeader><text><front></front><body></body><back><listBibl><biblStruct xml:id="b0"><analytic><title level="a" type="main">Perl dp. the evolution of diagnosis in early parkinson disease. 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