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L. Khedher, J. Ramírez, J. Górriz, A. Brahim, F. Segovia (2015)
Early diagnosis of Alzheimer's disease based on partial least squares, principal component analysis and support vector machine using segmented MRI imagesNeurocomputing, 151
J. Hanley, B. McNeil (1982)
The meaning and use of the area under a receiver operating characteristic (ROC) curve.Radiology, 143 1
Yi-Wei Chen, Chih-Jen Lin (2006)
Combining SVMs with Various Feature Selection Strategies
Markus Halldestam (2016)
ANOVA - The Effect of Outliers
S. Costafreda, C. Chu, J. Ashburner, C. Fu (2009)
Prognostic and Diagnostic Potential of the Structural Neuroanatomy of DepressionPLoS ONE, 4
(2016)
Scikit-Learn
F. Segovia, J. Górriz, J. Ramírez, D. Salas-González, Ignacio Illán, Míriam López, R. Chaves (2012)
A comparative study of feature extraction methods for the diagnosis of Alzheimer's disease using the ADNI databaseNeurocomputing, 75
H. Heijmans, J. Roerdink (1998)
Mathematical Morphology and its Applications to Image and Signal Processing
A. Rao, Ying Lee, A. Gass, A. Monsch (2011)
Classification of Alzheimer's Disease from structural MRI using sparse logistic regression with optional spatial regularization2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
(2014)
Global disease index, a novel tool for MTL atrophy assessment
Ignacio Illán, J. Górriz, J. Ramírez, D. Salas-González, M. López, F. Segovia, R. Chaves, M. Gómez-Río, C. Puntonet (2011)
18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosisInf. Sci., 181
L. Landini, V. Positano, M. Santarelli (2005)
Advanced Image Processing in Magnetic Resonance Imaging
(2003)
MARINA: an easy to use tool for the creation of MAsks for Region of INterest analyses
Benjamin Mast, B. Yochim (2017)
Alzheimer's Disease and Dementia
Ashish Gupta, M. Ayhan, A. Maida (2013)
Natural Image Bases to Represent Neuroimaging Data
C. Jack, M. Bernstein, Nick Fox, P. Thompson, G. Alexander, D. Harvey, B. Borowski, P. Britson, Jennifer Whitwell, C. Ward, A. Dale, J. Felmlee, J. Gunter, D. Hill, R. Killiany, N. Schuff, Sabrina Fox‐Bosetti, Chen Lin, C. Studholme, C. DeCarli, G. Krueger, H. Ward, G. Metzger, K. Scott, R. Mallozzi, D. Blezek, J. Levy, J. Debbins, A. Fleisher, M. Albert, R. Green, G. Bartzokis, G. Glover, J. Mugler, M. Weiner (2008)
The Alzheimer's disease neuroimaging initiative (ADNI): MRI methodsJournal of Magnetic Resonance Imaging, 27
Abhinit Ambastha (2015)
Neuroanatomical characterisation of Alzheimer ’ s disease using deep learning
tivariate analysis of variance ( MANOVA )
Imene Garali, M. Adel, S. Bourennane, E. Guedj (2016)
Brain region ranking for 18FDG-PET computer-aided diagnosis of Alzheimer's diseaseBiomed. Signal Process. Control., 27
V Fonov, AC Evans, K Botteron, CR Almli, RC McKinstry, DL Collins (2011)
Brain development cooperative group, others: unbiased average age-appropriate atlases for pediatric studiesNeuroImage, 54
A. Chincarini, P. Bosco, P. Calvini, G. Gemme, Mario Esposito, C. Olivieri, L. Rei, S. Squarcia, G. Rodriguez, R. Bellotti, P. Cerello, I. Mitri, A. Retico, F. Nobili (2011)
Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's diseaseNeuroImage, 58
Owen Carmichael, H. Aizenstein, S. Davis, J. Becker, P. Thompson, C. Meltzer, Yanxi Liu (2005)
Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairmentNeuroImage, 27
A. Payan, G. Montana (2015)
Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networksArXiv, abs/1502.02506
S. Eickhoff, K. Stephan, H. Mohlberg, C. Grefkes, G. Fink, K. Amunts, K. Zilles (2005)
A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging dataNeuroImage, 25
Wenlu Yang, Fangyu He, Xinyun Chen, Xudong Huang (2011)
ICA-Based Automatic Classification of PET Images from ADNI Database
S. Klöppel, C. Stonnington, J. Barnes, F. Chen, C. Chu, C. Good, I. Mader, L. Mitchell, Ameet Patel, C. Roberts, Nick Fox, C. Jack, J. Ashburner, Richard Frackowiak (2008)
Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized methodBrain, 131
J. Russ (2015)
The Image Processing Handbook
G. Vaiva, M. Walter, Abeer Arab, P. Courtet, F. Bellivier, A. Demarty, S. Duhem, F. Ducrocq, P. Goldstein, C. Libersa (2011)
ALGOS: the development of a randomized controlled trial testing a case management algorithm designed to reduce suicide risk among suicide attemptersBMC Psychiatry, 11
Siqi Liu, Sidong Liu, Weidong Cai, Hangyu Che, Sonia Pujol, R. Kikinis, D. Feng, M. Fulham (2015)
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's DiseaseIEEE Transactions on Biomedical Engineering, 62
R. Casanova, C. Whitlow, B. Wagner, J. Williamson, S. Shumaker, J. Maldjian, M. Espeland (2011)
High Dimensional Classification of Structural MRI Alzheimer’s Disease Data Based on Large Scale RegularizationFrontiers in Neuroinformatics, 5
Arno Klein, Jesper Andersson, B. Ardekani, John Ashburner, B. Avants, Ming-Chang Chiang, Gary Christensen, Louis Collins, Pierre Hellier, Hyun Song, Mark Jenkinson, Claude Lepage, D. Rueckert, Paul Thompson, Tom Vercauteren, Roger Woods, J. Mann, R. Parsey
Evaluation of 14 Nonlinear Deformation Algorithms Applied to Human Brain Mri Registration
A. Klein, J. Andersson, B. Ardekani, J. Ashburner, B. Avants, M. Chiang, G. Christensen, D. Collins, J. Gee, P. Hellier, J. Song, M. Jenkinson, C. Lepage, D. Rueckert, P. Thompson, T. Vercauteren, R. Woods, J. Mann, R. Parsey (2009)
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registrationNeuroImage, 46
L. Breiman (2001)
Random ForestsMachine Learning, 45
Karuppanagounder Somasundaram, T. Genish (2014)
The extraction of hippocampus from MRI of human brain using morphological and image binarization techniques2014 International Conference on Electronics and Communication Systems (ICECS)
V. Fonov, Alan Evans, K. Botteron, C. Almli, R. McKinstry, D. Collins (2011)
Unbiased average age-appropriate atlases for pediatric studiesNeuroImage, 54
S. Costafreda, C. Fu, M. Picchioni, T. Toulopoulou, C. Mcdonald, E. Kravariti, M. Walshe, D. Prata, R. Murray, P. McGuire (2011)
Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorderBMC Psychiatry, 11
J. Landis, G. Koch (1977)
The measurement of observer agreement for categorical data.Biometrics, 33 1
(2005)
BET2: MR-based estimation of brain, skull and scalp surfaces
Andreas Grünauer, M. Vincze (2015)
Using Dimension Reduction to Improve the Classification of High-dimensional DataArXiv, abs/1505.06907
(2017)
Dementia fact sheet
F. Segovia, J. Ramírez, J. Górriz, R. Chaves, D. Salas-González, Míriam López, Ignacio Illán, P. Padilla, C. Puntonet (2010)
Partial Least Squares for Feature Extraction of SPECT Images
Manhua Liu, Daoqiang Zhang, D. Shen (2013)
Identifying Informative Imaging Biomarkers via Tree Structured Sparse Learning for AD DiagnosisNeuroinformatics, 12
F. Chaumette (2004)
Image moments: a general and useful set of features for visual servoingIEEE Transactions on Robotics, 20
Wenlu Yang, R. Lui, Jia-Hong Gao, T. Chan, S. Yau, R. Sperling, Xudong Huang (2011)
Independent component analysis-based classification of Alzheimer's disease MRI data.Journal of Alzheimer's disease : JAD, 24 4
Nadir Elssied, O. Ibrahim, A. Osman (2014)
A Novel Feature Selection Based on One-Way ANOVA F-Test for E-Mail Spam ClassificationResearch Journal of Applied Sciences, Engineering and Technology, 7
B. Avants, C. Epstein, M. Grossman, J. Gee (2008)
Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brainMedical image analysis, 12 1
Abhishek Golugula, George Lee, A. Madabhushi (2011)
Evaluating feature selection strategies for high dimensional, small sample size datasets2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
A crucial quest in neuroimaging is the discovery of image features (biomarkers) associated with neurodegenerative disorders. Recent works show that such biomarkers can be obtained by image analysis techniques. However, these techniques cannot be directly compared since they use different databases and validation protocols. In this paper, we present an extensive study of image descriptors for the diagnosis of Alzheimer Disease (AD) and introduce a new one, named Residual Center of Mass (RCM). The RCM descriptor explores image moments and other techniques to enhance brain regions and select discriminative features for the diagnosis of AD. For validation, a Support Vector Machine (SVM) is trained with the selected features to classify images from normal subjects and patients with AD. We show that RCM with SVM achieves the best accuracies on a considerable number of exams by 10-fold cross-validation — 95.1% on 507 FDG-PET scans and 90.3% on 1374 MRI scans.
Neuroinformatics – Springer Journals
Published: Oct 17, 2018
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