Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images (488 views) (PDF restricted 230 views)
Comput Med Imag Grap (ISSN: 0895-6111), 2014 Jul; 38(5): 337-347.
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Paper type: Journal Article, Evaluation Studies, Research Support, Non-U. S. Gov'T,
Impact factor: 1.218, 5-year impact factor: 1.588
Url: http://www.scopus.com/inward/record.url?eid=2-s2.0-84902073537&partnerID=40&md5=3c415c19fdaf35b9e8de51bbf432507c
Keywords: Atlas-Based, Brain, Denoising, Discriminant Analysis, Segmentation, Subcortical Structures, Histology, Image Segmentation, Magnetic Resonance Imaging, Brain Magnetic Resonance Images, Classification Performance, De-Noising, Discriminant Analysis Methods, False Positive And False Negatives, Supervised Classification, Tissue, Article, False Negative Result, False Positive Result, Functional Neuroimaging, Imaging Phantom, K Nearest Neighbor, Kappa Statistics, Nuclear Magnetic Resonance, Priority Journal, Support Vector Machine, Brain Anatomy, Humans, Magnetic Resonance Imaging Methods,
Affiliations:
*** IBB - CNR ***
Istituto per le Applicazioni del Calcolo 'Mauro Picone' CNR, Napoli, Italy
Istituto di Biostrutture e Bioimmagini CNR, Napoli, Italy
Université catholique de Louvain, Department of Mathematical Engineering, Louvain-la-Neuve, Belgium
Universite catholique de Louvain, Department of Mathematical Engineering, Louvain-la-Neuve, Belgium.
Universit catholique de Louvain, Department of Mathematical Engineering, Louvain-la-Neuve, Belgium
References:
Not available.
Comput Med Imag Grap (ISSN: 0895-6111), 2014 Jul; 38(5): 337-347.
Export to BibTeX Export to EndNote
Paper type: Journal Article, Evaluation Studies, Research Support, Non-U. S. Gov'T,
Impact factor: 1.218, 5-year impact factor: 1.588
Url: http://www.scopus.com/inward/record.url?eid=2-s2.0-84902073537&partnerID=40&md5=3c415c19fdaf35b9e8de51bbf432507c
Keywords: Atlas-Based, Brain, Denoising, Discriminant Analysis, Segmentation, Subcortical Structures, Histology, Image Segmentation, Magnetic Resonance Imaging, Brain Magnetic Resonance Images, Classification Performance, De-Noising, Discriminant Analysis Methods, False Positive And False Negatives, Supervised Classification, Tissue, Article, False Negative Result, False Positive Result, Functional Neuroimaging, Imaging Phantom, K Nearest Neighbor, Kappa Statistics, Nuclear Magnetic Resonance, Priority Journal, Support Vector Machine, Brain Anatomy, Humans, Magnetic Resonance Imaging Methods,
Affiliations:
*** IBB - CNR ***
Istituto per le Applicazioni del Calcolo 'Mauro Picone' CNR, Napoli, Italy
Istituto di Biostrutture e Bioimmagini CNR, Napoli, Italy
Université catholique de Louvain, Department of Mathematical Engineering, Louvain-la-Neuve, Belgium
Universite catholique de Louvain, Department of Mathematical Engineering, Louvain-la-Neuve, Belgium.
Universit catholique de Louvain, Department of Mathematical Engineering, Louvain-la-Neuve, Belgium
References:
Not available.
Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images
This work investigates the capability of supervised classification methods in detecting both major tissues and subcortical structures using multispectral brain magnetic resonance images. First, by means of a realistic digital brain phantom, we investigated the classification performance of various Discriminant Analysis methods, K-Nearest Neighbor and Support Vector Machine. Then, using phantom and real data, we quantitatively assessed the benefits of integrating anatomical information in the classification, in the form of voxels coordinates as additional features to the intensities or tissue probabilistic atlases as priors. In addition we tested the effect of spatial correlations between neighboring voxels and image denoising. For each brain tissue we measured the classification performance in terms of global agreement percentage, false positive and false negative rates and kappa coefficient. The effectiveness of integrating spatial information or a tissue probabilistic atlas has been demonstrated for the aim of accurately classifying brain magnetic resonance images. © 2014 Elsevier Ltd.
This work investigates the capability of supervised classification methods in detecting both major tissues and subcortical structures using multispectral brain magnetic resonance images. First, by means of a realistic digital brain phantom, we investigated the classification performance of various Discriminant Analysis methods, K-Nearest Neighbor and Support Vector Machine. Then, using phantom and real data, we quantitatively assessed the benefits of integrating anatomical information in the classification, in the form of voxels coordinates as additional features to the intensities or tissue probabilistic atlases as priors. In addition we tested the effect of spatial correlations between neighboring voxels and image denoising. For each brain tissue we measured the classification performance in terms of global agreement percentage, false positive and false negative rates and kappa coefficient. The effectiveness of integrating spatial information or a tissue probabilistic atlas has been demonstrated for the aim of accurately classifying brain magnetic resonance images. © 2014 Elsevier Ltd.
Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images
Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images
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View Export to BibTeX Export to EndNote
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View Export to BibTeX Export to EndNote
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Impact Factor: 1.777
View Export to BibTeX Export to EndNote
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View Export to BibTeX Export to EndNote
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View Export to BibTeX Export to EndNote
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View Export to BibTeX Export to EndNote
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Impact Factor: 1.302
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Brunetti A, Postiglione A, Tedeschi E, Ciarmiello A, Quarantelli M, Covelli E, Milan G, Larobina M, Soricelli A, Sodano A, Alfano B
Measurement of global drain atrophy in Alzheimer's disease with unsupervised segmentation of spin-echo MRI studies (439 views)
J Magn Reson Imaging (ISSN: 1053-1807, 1522-2586electronic), 2000 Mar; 11(3): N/D-N/D.
Impact Factor: 1.302
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Larobina M, Quarantelli M, Tedeschi E, Brunetti A, Alfano B, Salvatore M
Validation of an automated regional brain segmentation method using a stereotactic Talairach approach (467 views)
Surgery International Congress Series Car 97 (ISSN: 0531-5131), 1999; 1191: N/D-N/D.
Impact Factor: 1.438
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Pace L, Perrone-Filardi P, Mainenti PP, Prastaro M, Vezzuto P, Varrone A, Crisci T, Cuocolo A, Dellegrottaglie S, Piscione F, Chiariello M, Salvatore M
Combined evaluation of rest-redistribution thallium-201 tomography and low-dose dobutamine echocardiography enhances the identification of viable myocardium in patients with chronic coronary artery disease (444 views)
Eur J Nucl Med (ISSN: 0340-6997, 1619-7070, 1619-7089), 1998 Jul; 25(7): 744-750.
Impact Factor: 2.564
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Alfano B, Quarantelli M, Brunetti A, Larobina M, Covelli EM, Tedeschi E, Salvatore M
Reproducibility of intracranial volume measurement by unsupervised multispectral brain segmentation (708 views) (PDF 601 views)
Magn Reson Med (ISSN: 0740-3194, 1522-2594electronic, 0740-3194linking), 1998 Mar; 39(3): 497-499.
Impact Factor: 2.962
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Alfano B, Brunetti A, Covelli EM, Quarantelli M, Panico M, Ciarmiello A, Salvatore M
Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach (682 views) (PDF 699 views)
Magn Reson Med (ISSN: 0740-3194, 1522-2594electronic, 0740-3194linking), 1997 Jan; 37(1): 84-93.
Impact Factor: 3.012
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Alfano B, Brunetti A, Quarantelli M, Larobina M, Covelli EM, Tedeschi E, Ciarmiello A, Salvatore M
Fully-automated multispectral MR brain segmentation for detection and volumetric measurement of white matter lesions (377 views)
Surgery International Congress Series Car 97 (ISSN: 0531-5131), 1997; 1134: N/D-N/D.
Impact Factor: 1.438
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Brunetti A, Covelli E, Tedeschi E, Quarantelli M, Soricelli A, Salvatore M, Alfano B
Unsupervised multifeature MR segmentation for assessment of intracranial liquoral spaces: Comparison with conventional linear measures (375 views)
Surgery International Congress Series Car 97 (ISSN: 0531-5131), 1996; 1124: N/D-N/D.
Impact Factor: 1.438
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ALFANO B, ARPAIA M, BRUNETTI A, COVELLI E, CASTELLI L, CARACO C, SORICELLI A, TEDESCHI E, SALVATORE M
Automatic segmentation and evaluation of the volume of gray matter, white matter and cerebrospinal fluid by MRI (339 views)
Neuroradiology 1994, 1994; N/D: N/D-N/D.
Impact Factor: 0
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Nicolai E, Cuocolo A, Pace L, Maurea S, Nappi A, Imbriaco M, Cardei S, Morisco C, Argenziano L, Salvatore M
Ventricular systolic wall thickening by Technetium-99m methoxy-isobutyl-isonitrile. Relationship with coronary anatomy (272 views)
Radiol Med (ISSN: 0033-8362, 1826-6983, 1826-6983electronic), 1994; 88(1-2): 100-106.
Impact Factor: 0.967
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32 Records (21 excluding Abstracts).
Total impact factor: 70.566 (54.155 excluding Abstracts).
Total 5 year impact factor: 86.24 (63.418 excluding Abstracts).
Total impact factor: 70.566 (54.155 excluding Abstracts).
Total 5 year impact factor: 86.24 (63.418 excluding Abstracts).
488 views. Last view on Monday 20 November 2023, 2:09:34
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