Classification of neurodegenerative dementia by Gaussian Mixture Models applied to SPECT images
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
EU-Projektnummer
DFG-Projektnummer
Projekt
Open Access-Veröffentlichung
Sammlungen
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
unikn.publication.listelement.citation.prefix.version.undefined
Zusammenfassung
Gaussian mixture (GM) models can be applied for statistical classification of various types of dementia. As opposed to linear boundaries, they do not only provide the class membership of a case, but also a measure of its probability. This enables an improved interpretation and classification of neurodegenerative dementia datasets which comprise various stages of the disease, and also mixed forms of dementia. In this work, GM models are applied to a total number of 103 technetium-99methylcysteinatedimer (99mTc-ECD) SPECT datasets of asymptomatic controls (CTR), as well as Alzheimer’s disease (AD) and frontotemporal dementia (FTD) patients in early or moderate stages of the disease. Prior to classification, multivariate analysis is applied: Principal component analysis (PCA) is used for dimensionality reduction, followed by a differentiation of the datasets via multiple discriminant analysis (MDA). A GM model on the resulting discrimination plane is constructed by computing the GM distribution associated with the underlying training set. The posterior probabilities of each case indicate its class membership probability. The performance of GM models for classification is assessed by bootstrap resampling and cross validation. Accuracy and robustness of the method are evaluated for different numbers of principal components (PCs), and furthermore the detection rate of dementia in early stages is calculated. The GM model outperfomes classification with linear boundaries in both predicted accuracy and detection rate of early dementia, and is equally robust.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
STÜHLER, Elisabeth, Günther PLATSCH, Markus WEIH, Johannes KORNHUBER, Torsten KUWERT, Dorit MERHOF, 2012. Classification of neurodegenerative dementia by Gaussian Mixture Models applied to SPECT images. 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (2012 NSS/MIC). Anaheim, CA, USA, 27. Okt. 2012 - 3. Nov. 2012. In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC). IEEE, 2012, pp. 3165-3169. ISBN 978-1-4673-2028-3. Available under: doi: 10.1109/NSSMIC.2012.6551722BibTex
@inproceedings{Stuhler2012-10Class-21451, year={2012}, doi={10.1109/NSSMIC.2012.6551722}, title={Classification of neurodegenerative dementia by Gaussian Mixture Models applied to SPECT images}, isbn={978-1-4673-2028-3}, publisher={IEEE}, booktitle={2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC)}, pages={3165--3169}, author={Stühler, Elisabeth and Platsch, Günther and Weih, Markus and Kornhuber, Johannes and Kuwert, Torsten and Merhof, Dorit} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/21451"> <dcterms:issued>2012-10</dcterms:issued> <dc:contributor>Kornhuber, Johannes</dc:contributor> <dcterms:bibliographicCitation>2012 IEEE Nuclear Science Symposium and Medical Imaging Conference : (NSS/MIC 2012) ; October 29 - November 3, 2012, Disneyland Hotel, Anaheim, California ; and International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors / Yu, Bo [Guest Ed.]. - Piscataway, NJ : IEEE, 2012. - S. 3165-3169. - ISBN 978-1-4673-2029-0</dcterms:bibliographicCitation> <dc:rights>terms-of-use</dc:rights> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-02-26T11:00:14Z</dcterms:available> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:contributor>Weih, Markus</dc:contributor> <dc:language>eng</dc:language> <dcterms:title>Classification of neurodegenerative dementia by Gaussian Mixture Models applied to SPECT images</dcterms:title> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-02-26T11:00:14Z</dc:date> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:creator>Platsch, Günther</dc:creator> <dc:creator>Weih, Markus</dc:creator> <dc:contributor>Merhof, Dorit</dc:contributor> <dc:creator>Kornhuber, Johannes</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dc:creator>Stühler, Elisabeth</dc:creator> <dcterms:abstract xml:lang="eng">Gaussian mixture (GM) models can be applied for statistical classification of various types of dementia. As opposed to linear boundaries, they do not only provide the class membership of a case, but also a measure of its probability. This enables an improved interpretation and classification of neurodegenerative dementia datasets which comprise various stages of the disease, and also mixed forms of dementia. In this work, GM models are applied to a total number of 103 technetium-99methylcysteinatedimer (99mTc-ECD) SPECT datasets of asymptomatic controls (CTR), as well as Alzheimer’s disease (AD) and frontotemporal dementia (FTD) patients in early or moderate stages of the disease. Prior to classification, multivariate analysis is applied: Principal component analysis (PCA) is used for dimensionality reduction, followed by a differentiation of the datasets via multiple discriminant analysis (MDA). A GM model on the resulting discrimination plane is constructed by computing the GM distribution associated with the underlying training set. The posterior probabilities of each case indicate its class membership probability. The performance of GM models for classification is assessed by bootstrap resampling and cross validation. Accuracy and robustness of the method are evaluated for different numbers of principal components (PCs), and furthermore the detection rate of dementia in early stages is calculated. The GM model outperfomes classification with linear boundaries in both predicted accuracy and detection rate of early dementia, and is equally robust.</dcterms:abstract> <dc:contributor>Kuwert, Torsten</dc:contributor> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:contributor>Stühler, Elisabeth</dc:contributor> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> <dc:contributor>Platsch, Günther</dc:contributor> <dc:creator>Kuwert, Torsten</dc:creator> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/21451"/> <dc:creator>Merhof, Dorit</dc:creator> </rdf:Description> </rdf:RDF>