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NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer's disease.

Identifieur interne : 000319 ( PubMed/Corpus ); précédent : 000318; suivant : 000320

NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer's disease.

Auteurs : P. Padilla ; M. L Pez ; J M G Rriz ; J. Ramírez ; D. Salas-González ; I. Álvarez

Source :

IEEE transactions on medical imaging [ 1558-254X ] ; 2012.

RBID : pubmed:21914569

English descriptors

KwdEn :
- Algorithms, Alzheimer Disease (radionuclide imaging), Brain Mapping (methods), Humans, Image Enhancement (methods), Image Interpretation, Computer-Assisted (methods), Pattern Recognition, Automated (methods), Radionuclide Imaging (methods), Reproducibility of Results, Sensitivity and Specificity, Support Vector Machine.
MESH :
- methods : Brain Mapping, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Radionuclide Imaging.
- radionuclide imaging : Alzheimer Disease.
- Algorithms, Humans, Reproducibility of Results, Sensitivity and Specificity, Support Vector Machine.

Abstract

This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimer's disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) for feature selection and extraction of the most relevant features. The resulting NMF-transformed sets of data, which contain a reduced number of features, are classified by means of a SVM-based classifier with bounds of confidence for decision. The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification.

DOI: 10.1109/TMI.2011.2167628
PubMed: 21914569

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pubmed:21914569

Le document en format XML

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<author><name sortKey="Padilla, P" sort="Padilla, P" uniqKey="Padilla P" first="P" last="Padilla">P. Padilla</name>
<affiliation><nlm:affiliation>Department of Signal Theory, Telematics and Communications, University of Granada, 18071 Granada, Spain. ppadilla00@gr.ssr.upm.es</nlm:affiliation>
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<author><name sortKey="L Pez, M" sort="L Pez, M" uniqKey="L Pez M" first="M" last="L Pez">M. L Pez</name>
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<author><name sortKey="G Rriz, J M" sort="G Rriz, J M" uniqKey="G Rriz J" first="J M" last="G Rriz">J M G Rriz</name>
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<author><name sortKey="Ramirez, J" sort="Ramirez, J" uniqKey="Ramirez J" first="J" last="Ramírez">J. Ramírez</name>
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<author><name sortKey="Salas Gonzalez, D" sort="Salas Gonzalez, D" uniqKey="Salas Gonzalez D" first="D" last="Salas-González">D. Salas-González</name>
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<author><name sortKey="Alvarez, I" sort="Alvarez, I" uniqKey="Alvarez I" first="I" last="Álvarez">I. Álvarez</name>
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<term>Image Enhancement (methods)</term>
<term>Image Interpretation, Computer-Assisted (methods)</term>
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<term>Radionuclide Imaging</term>
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<front><div type="abstract" xml:lang="en">This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimer's disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) for feature selection and extraction of the most relevant features. The resulting NMF-transformed sets of data, which contain a reduced number of features, are classified by means of a SVM-based classifier with bounds of confidence for decision. The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification.</div>
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<Abstract><AbstractText>This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimer's disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) for feature selection and extraction of the most relevant features. The resulting NMF-transformed sets of data, which contain a reduced number of features, are classified by means of a SVM-based classifier with bounds of confidence for decision. The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification.</AbstractText>
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<MeshHeading><DescriptorName MajorTopicYN="N" UI="D012680">Sensitivity and Specificity</DescriptorName>
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NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer's disease.

NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer's disease.

Source :

English descriptors

Abstract

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