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Automatic ROI selection in structural brain MRI using SOM 3D projection.

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to eac... Full description

Journal Title: PloS one 2014, Vol.9(4), p.e93851
Main Author: Ortiz, Andrés
Other Authors: Górriz, Juan M , Ramírez, Javier , Martinez-Murcia, Francisco J , Ortiz, Andrés
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0093851
Link: http://search.proquest.com/docview/1516729042/?pq-origsite=primo
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title: Automatic ROI selection in structural brain MRI using SOM 3D projection.
format: Article
creator:
  • Ortiz, Andrés
  • Górriz, Juan M
  • Ramírez, Javier
  • Martinez-Murcia, Francisco J
  • Ortiz, Andrés
subjects:
  • Aged–Pathology
  • Aged, 80 and Over–Pathology
  • Algorithms–Methods
  • Alzheimer Disease–Methods
  • Brain–Methods
  • Female–Methods
  • Humans–Methods
  • Magnetic Resonance Imaging–Methods
  • Male–Methods
ispartof: PloS one, 2014, Vol.9(4), p.e93851
description: This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.
language: eng
source:
identifier: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0093851
fulltext: fulltext
issn:
  • 19326203
  • 1932-6203
url: Link


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titleAutomatic ROI selection in structural brain MRI using SOM 3D projection.
creatorOrtiz, Andrés ; Górriz, Juan M ; Ramírez, Javier ; Martinez-Murcia, Francisco J ; Ortiz, Andrés
contributorWeiner, Michael W (correspondence author) ; Aisen, Paul (record owner) ; Weiner, Michael ; Aisen, Paul ; Petersen, Ronald ; Jack, Clifford R ; Jagust, William ; Trojanowki, John Q ; Toga, Arthur W ; Beckett, Laurel ; Green, Robert C ; Saykin, Andrew J ; Morris, John ; Shaw, Leslie M ; Khachaturian, Zaven ; Sorensen, Greg ; Carrillo, Maria ; Kuller, Lew ; Raichle, Marc ; Paul
ispartofPloS one, 2014, Vol.9(4), p.e93851
identifierE-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0093851
subjectAged–Pathology ; Aged, 80 and Over–Pathology ; Algorithms–Methods ; Alzheimer Disease–Methods ; Brain–Methods ; Female–Methods ; Humans–Methods ; Magnetic Resonance Imaging–Methods ; Male–Methods
languageeng
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descriptionThis paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.
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58Korecka, Magdalena
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67Nho, Kwangsik
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74Hsiao, John
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76Quinn, Joseph
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78Carter, Raina
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85Brewer, James
86Vanderswag, Helen
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96Chowdhury, Munir
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titleAutomatic ROI selection in structural brain MRI using SOM 3D projection.
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37Jiminez, Gus
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40Fox, Nick
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42Schuff, Norbert
43Decarli, Charles
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45Gunter, Jeff
46Senjem, Matt
47Vemuri, Prashanthi
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52Foster, Norm
53Reiman, Eric M
54Chen, Kewei
55Mathis, Chet
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57Morris, John C
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65Neu, Scott
66Foroud, Tatiana M
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69Faber, Kelley
70Kim, Sungeun
71Nho, Kwangsik
72Thal, Lean
73Thal, Leon
74Buckholtz, Neil
75Snyder, Peter J
76Albert, Marylyn
77Frank, Richard
78Hsiao, John
79Kaye, Jeffrey
80Quinn, Joseph
81Lind, Betty
82Carter, Raina
83Dolen, Sara
84Schneider, Lon S
85Pawluczyk, Sonia
86Beccera, Mauricio
87Teodoro, Liberty
88Spann, Bryan M
89Brewer, James
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91Fleisher, Adam
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93Lord, Joanne L
94Mason, Sara S
95Albers, Colleen S
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31Gessert, Devon
32Sather, Tamie
33Jiminez, Gus
34Harvey, Danielle
35Bernstein, Matthew
36Fox, Nick
37Thompson, Paul
38Schuff, Norbert
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65Faber, Kelley
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80Schneider, Lon S
81Pawluczyk, Sonia
82Beccera, Mauricio
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96Chowdhury, Munir
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99Stern, Yaakov
100...
atitleAutomatic ROI selection in structural brain MRI using SOM 3D projection.
jtitlePloS one
risdate20140101
volume9
issue4
spagee93851
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doi10.1371/journal.pone.0093851
urlhttp://search.proquest.com/docview/1516729042/
date2014-01-01