Structural MRI - based classification of alzheimer's disease

Doctor of Philosophy in Electrical and Electronic Engineering. Thesis (Ph.D.)--Eastern Mediterranean University, Faculty of Engineering, Dept. of Electrical and Electronic Engineering, 2016. Supervisor: Prof. Dr. Hasan Demirel.

[1]  Daoqiang Zhang,et al.  Identification of MCI individuals using structural and functional connectivity networks , 2012, NeuroImage.

[2]  A. Simmons,et al.  Sensitivity and Specificity of Medial Temporal Lobe Visual Ratings and Multivariate Regional MRI Classification in Alzheimer's Disease , 2011, PloS one.

[3]  Yutaka Kuroda,et al.  DROP: an SVM domain linker predictor trained with optimal features selected by random forest , 2011, Bioinform..

[4]  Francisco Jesús Martínez-Murcia,et al.  LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease , 2013, Pattern Recognit. Lett..

[5]  Hiroshi Matsuda,et al.  Discrimination of dementia with Lewy bodies from Alzheimer’s disease using voxel-based morphometry of white matter by statistical parametric mapping 8 plus diffeomorphic anatomic registration through exponentiated Lie algebra , 2013, Neuroradiology.

[6]  Mehdi Moradi,et al.  A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI , 2015, Comput. Medical Imaging Graph..

[7]  E. Luders,et al.  Voxel-Based Morphometry , 2015 .

[8]  T. Wieczorek,et al.  Comparison of feature ranking methods based on information entropy , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[9]  Nick C Fox,et al.  Automatic classification of MR scans in Alzheimer's disease. , 2008, Brain : a journal of neurology.

[10]  Douglas N. Greve,et al.  An Absolute Beginner's Guide to Surface- and Voxel-based Morphometric Analysis , 2011 .

[11]  Li Yao,et al.  Multi-modality sparse representation-based classification for Alzheimer's disease and mild cognitive impairment , 2015, Comput. Methods Programs Biomed..

[12]  Margarida Silveira,et al.  Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages , 2015, Comput. Biol. Medicine.

[13]  Xiangliang Zhang,et al.  Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection , 2014, IEEE Transactions on Information Forensics and Security.

[14]  Glenn Fung,et al.  SVM Feature Selection for Classification of SPECT Images of Alzheimer's Disease Using Spatial Information , 2005, ICDM.

[15]  Jorge S. Marques,et al.  Boosting Alzheimer Disease Diagnosis Using PET Images , 2010, 2010 20th International Conference on Pattern Recognition.

[16]  Bernard Zenko,et al.  Evaluation Method for Feature Rankings and their Aggregations for Biomarker Discovery , 2009, MLSB.

[17]  Eric Westman,et al.  Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion , 2012, NeuroImage.

[18]  Jin Wang,et al.  An Optimal Decisional Space for the Classification of Alzheimer's Disease and Mild Cognitive Impairment , 2014, IEEE Transactions on Biomedical Engineering.

[19]  Pradeep Reddy Raamana,et al.  Comparison of four shape features for detecting hippocampal shape changes in early Alzheimer's , 2013, Statistical methods in medical research.

[20]  Maximilian Reiser,et al.  Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment , 2007, NeuroImage.

[21]  G. Stebbins,et al.  Diffusion Tensor Imaging in Alzheimer’s Disease and Mild Cognitive Impairment , 2009, Behavioural neurology.

[22]  Brigitte Landeau,et al.  Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: A longitudinal MRI study , 2005, NeuroImage.

[23]  Andrea Chincarini,et al.  Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease , 2011, NeuroImage.

[24]  Bianca Zadrozny,et al.  A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer's disease and mild cognitive impairment , 2014, Comput. Biol. Medicine.

[25]  Achim Zeileis,et al.  Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.

[26]  H. Saji,et al.  Novel PET/SPECT Probes for Imaging of Tau in Alzheimer's Disease , 2015, TheScientificWorldJournal.

[27]  Edward R. Dougherty,et al.  High-dimensional bolstered error estimation , 2011, Bioinform..

[28]  Zhizhong Wang,et al.  The application of mutual information-based feature selection and fuzzy LS-SVM-based classifier in motion classification , 2008, Comput. Methods Programs Biomed..

[29]  Glenn Fung,et al.  SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[30]  Charles D. Smith,et al.  Partial least squares for discrimination in fMRI data. , 2012, Magnetic resonance imaging.

[31]  A. Simmons,et al.  Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment , 2013, Psychiatry Research: Neuroimaging.

[32]  Reinout W. Wiers,et al.  Grey matter alterations associated with cannabis use: Results of a VBM study in heavy cannabis users and healthy controls , 2012, NeuroImage.

[33]  Gholamreza Anbarjafari,et al.  Pose Invariant Face Recognition Using Probability Distribution Functions in Different Color Channels , 2008, IEEE Signal Processing Letters.

[34]  Vikas Singh,et al.  Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population , 2011, NeuroImage.

[35]  Byungkyu Brian Park,et al.  Classification of diffusion tensor images for the early detection of Alzheimer's disease , 2013, Comput. Biol. Medicine.

[36]  Emma J. Burton,et al.  A comprehensive study of gray matter loss in patients with Alzheimer’s disease using optimized voxel-based morphometry , 2003, NeuroImage.

[37]  D. Louis Collins,et al.  MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls , 2008, IEEE Transactions on Medical Imaging.

[38]  Ramon Casanova,et al.  Evaluating the Impact of Different Factors on Voxel-Based Classification Methods of ADNI Structural MRI Brain Images , 2011 .

[39]  Okko Johannes Räsänen,et al.  Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits , 2015, Comput. Speech Lang..

[40]  J. Pariente,et al.  Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve , 2009, Brain : a journal of neurology.

[41]  H. Benali,et al.  Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI , 2009, Hippocampus.

[42]  Philippe Robert,et al.  Classification of SPECT Images of Normal Subjects versus Images of Alzheimer's Disease Patients , 2001, MICCAI.

[43]  Xiao Liu,et al.  Fast multi-view segment graph kernel for object classification , 2013, Signal Process..

[44]  F. Segovia,et al.  SPECT image classification based on NMSE feature correlation weighting and SVM , 2009, 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC).

[45]  Sidong Liu,et al.  Article in Press G Model Computerized Medical Imaging and Graphics Multi-channel Neurodegenerative Pattern Analysis and Its Application in Alzheimer's Disease Characterization , 2022 .

[46]  Svetha Venkatesh,et al.  Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso , 2015, J. Biomed. Informatics.

[47]  S. Khushu,et al.  Effect of visual experience on structural organization of the human brain: a voxel based morphometric study using DARTEL. , 2012, European journal of radiology.

[48]  Marie Chupin,et al.  Automatic classi fi cation of patients with Alzheimer ' s disease from structural MRI : A comparison of ten methods using the ADNI database , 2010 .

[49]  Sung-Nien Yu,et al.  Conditional mutual information-based feature selection for congestive heart failure recognition using heart rate variability , 2012, Comput. Methods Programs Biomed..

[50]  Dinggang Shen,et al.  Morphological classification of brains via high-dimensional shape transformations and machine learning methods , 2004, NeuroImage.

[51]  David J. Hand,et al.  Measuring classifier performance: a coherent alternative to the area under the ROC curve , 2009, Machine Learning.

[52]  Lipo Wang,et al.  A Modified T-test Feature Selection Method and Its Application on the HapMap Genotype Data , 2008, Genom. Proteom. Bioinform..

[53]  Tao Qin,et al.  Feature selection for ranking , 2007, SIGIR.

[54]  Jie Tian,et al.  Neural specificity of acupuncture stimulation from support vector machine classification analysis. , 2011, Magnetic resonance imaging.

[55]  Daoqiang Zhang,et al.  Multimodal classification of Alzheimer's disease and mild cognitive impairment , 2011, NeuroImage.

[56]  Manuel Graña,et al.  A lattice computing approach to Alzheimer's disease computer assisted diagnosis based on MRI data , 2015, Neurocomputing.

[57]  Kuncheng Li,et al.  Voxel-based assessment of gray and white matter volumes in Alzheimer's disease , 2010, Neuroscience Letters.

[58]  Edward R. Dougherty,et al.  Superior feature-set ranking for small samples using bolstered error estimation , 2005, Bioinform..

[59]  Keiji Hashimoto,et al.  Voxel- and atlas-based analysis of diffusion tensor imaging may reveal focal axonal injuries in mild traumatic brain injury -- comparison with diffuse axonal injury. , 2012, Magnetic resonance imaging.

[60]  Yin J. Chen,et al.  A semi-quantitative method for correlating brain disease groups with normal controls using SPECT: Alzheimer's disease versus vascular dementia , 2013, Comput. Medical Imaging Graph..

[61]  Juan Manuel Górriz,et al.  Early diagnosis of Alzheimer's disease based on Partial Least Squares and Support Vector Machine , 2013, Expert Syst. Appl..

[62]  Xiaomu Song,et al.  A SVM-based quantitative fMRI method for resting-state functional network detection. , 2014, Magnetic resonance imaging.

[63]  B. Kee,et al.  Correlation between gray matter volume in the temporal lobe and depressive symptoms in patients with Alzheimer's disease , 2013, Neuroscience Letters.

[64]  Toshihiko Iwamoto,et al.  The progression of cognitive deterioration and regional cerebral blood flow patterns in Alzheimer's disease: A longitudinal SPECT study , 2010, Journal of the Neurological Sciences.

[65]  C. Sorg,et al.  Prediction of Alzheimer's disease using individual structural connectivity networks , 2012, Neurobiology of Aging.

[66]  Edward Challis,et al.  Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI , 2015, NeuroImage.

[67]  John Ashburner,et al.  Computational anatomy with the SPM software. , 2009, Magnetic resonance imaging.

[68]  F. Shi,et al.  Hippocampal Shape Analysis of Alzheimer Disease Based on Machine Learning Methods , 2007, American Journal of Neuroradiology.

[69]  Juan Manuel Górriz,et al.  GMM based SPECT image classification for the diagnosis of Alzheimer's disease , 2011, Appl. Soft Comput..

[70]  Manuel Graña,et al.  Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI , 2011, Comput. Biol. Medicine.

[71]  Daoqiang Zhang,et al.  Ensemble sparse classification of Alzheimer's disease , 2012, NeuroImage.

[72]  Sašo Džeroski,et al.  Improved medical image modality classification using a combination of visual and textual features , 2015, Comput. Medical Imaging Graph..

[73]  Silong Peng,et al.  A New Method Combining LDA and PLS for Dimension Reduction , 2014, PloS one.

[74]  Daniel Rueckert,et al.  Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease , 2012, NeuroImage.

[75]  Huan Liu,et al.  Advancing feature selection research , 2010 .

[76]  Anand M. Narasimhamurthy.  Theoretical bounds of majority voting performance for a binary classification problem , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[77]  Omar S. Al-Kadi,et al.  A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours , 2015, Comput. Medical Imaging Graph..

[78]  Deqing Wang,et al.  t-Test feature selection approach based on term frequency for text categorization , 2014, Pattern Recognit. Lett..

[79]  A. Besga,et al.  Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation , 2011, Neuroscience Letters.

[80]  Fei Gao,et al.  Discriminative analysis of multivariate features from structural MRI and diffusion tensor images. , 2014, Magnetic resonance imaging.

[81]  J. Baron,et al.  In Vivo Mapping of Gray Matter Loss with Voxel-Based Morphometry in Mild Alzheimer's Disease , 2001, NeuroImage.

[82]  Ramin V. Parsey,et al.  Multimodal classification of Dementia using functional data, anatomical features and 3D invariant shape descriptors , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[83]  H Matsuda,et al.  Automatic Voxel-Based Morphometry of Structural MRI by SPM8 plus Diffeomorphic Anatomic Registration Through Exponentiated Lie Algebra Improves the Diagnosis of Probable Alzheimer Disease , 2012, American Journal of Neuroradiology.

[84]  Weizhong Yan,et al.  Fusion in multi-criterion feature ranking , 2007, 2007 10th International Conference on Information Fusion.

[85]  Marie Chupin,et al.  Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging , 2009, NeuroImage.

[86]  Jin Zhang,et al.  The Diagnosis of Alzheimer's Disease Based on Voxel-Based Morphometry and Support Vector Machine , 2008, 2008 Fourth International Conference on Natural Computation.

[87]  Karsten Mueller,et al.  Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI , 2013, Psychiatry Research: Neuroimaging.

[88]  H. Demirel,et al.  IMPROVED PDF BASED FACE RECOGNITION USING DATA FUSION , 2012 .

[89]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[90]  Chokri Ben Amar,et al.  Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features , 2014, Multimedia Tools and Applications.

[91]  Serge Kinkingnehun,et al.  DTI and Structural MRI Classification in Alzheimer’s Disease , 2012 .

[92]  Anna Kalbarczyk,et al.  Sex and gender differences in Alzheimer's disease: recommendations for future research. , 2012, Journal of women's health.

[93]  Vladimir Fonov,et al.  Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge , 2015, NeuroImage.

[94]  Alan C. Evans,et al.  Fast and robust parameter estimation for statistical partial volume models in brain MRI , 2004, NeuroImage.

[95]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[96]  Clifford R. Jack,et al.  Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies , 2008, NeuroImage.

[97]  et al.,et al.  Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline , 2008, NeuroImage.

[98]  Chih-Jen Lin,et al.  Feature Ranking Using Linear SVM , 2008, WCCI Causation and Prediction Challenge.