A Novel Texture Extraction Technique with T1 Weighted MRI for the Classification of Alzheimer’s Disease

[1]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[2]  Yee-Hong Yang,et al.  Robust Volumetric Texture Classification of Magnetic Resonance Images of the Brain Using Local Frequency Descriptor , 2014, IEEE Transactions on Image Processing.

[3]  K. R. Anandh,et al.  Analysis of Alzheimer Condition in T1-Weighted MR Images Using Texture Features and K-NN Classifier , 2018, 2018 International CET Conference on Control, Communication, and Computing (IC4).

[4]  Juan Manuel Górriz,et al.  Early diagnosis of Alzheimer's disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images , 2015, Neurocomputing.

[5]  Mona K. Beyer,et al.  MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression , 2017, Front. Aging Neurosci..

[6]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[7]  D. Selvathi,et al.  MRI brain pattern analysis for detection of Alzheimer's disease using random forest classifier , 2016, Intell. Decis. Technol..

[8]  G. Busatto,et al.  Volumetric reduction of the corpus callosum in Alzheimer's disease in vivo as assessed with voxel-based morphometry , 2007, Psychiatry Research: Neuroimaging.

[9]  Anders H. Andersen,et al.  Alterations in multiple measures of white matter integrity in normal women at high risk for Alzheimer's disease , 2010, NeuroImage.

[10]  Sherine Rady,et al.  Statistical features and voxel-based morphometry for alzheimer's disease classification , 2018, 2018 9th International Conference on Information and Communication Systems (ICICS).

[11]  Jianhua Dai,et al.  Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification , 2013, Appl. Soft Comput..

[12]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[13]  Juan Manuel Górriz,et al.  Texture Features Based Detection of Parkinson's Disease on DaTSCAN Images , 2013, IWINAC.

[14]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease , 1984, Neurology.

[15]  Masayuki Matsuda,et al.  Four subgroups of Alzheimer's disease based on patterns of atrophy using VBM and a unique pattern for early onset disease , 2006, NeuroImage.

[16]  Annalena Venneri,et al.  Neuroanatomical correlates of neuropsychiatric symptoms in Alzheimer's disease. , 2008, Brain : a journal of neurology.

[17]  Guy Marchal,et al.  Automated multi-modality image registration based on information theory , 1995 .

[18]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[19]  Tanner Koomar,et al.  M1: Cerebroviz: An R Package For Anatomical Visualization of Spatiotemporal Brain Data , 2017, European Neuropsychopharmacology.

[20]  Sanjay Kalra,et al.  Voxel-Based Texture Analysis of the Brain , 2015, PloS one.

[21]  Zezhi Chen,et al.  Detecting and Classifying Linear Structures in Mammograms Using Random Forests , 2011, IPMI.

[22]  Stephen Kwek,et al.  Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.

[23]  Gerhard Tutz,et al.  Penalized regression with correlation-based penalty , 2009, Stat. Comput..

[24]  L. Yao,et al.  Multimodal Classification of Mild Cognitive Impairment Based on Partial Least Squares. , 2016, Journal of Alzheimer's disease : JAD.

[25]  Charles D. Smith,et al.  White matter integrity and vulnerability to Alzheimer's disease: preliminary findings and future directions. , 2012, Biochimica et biophysica acta.

[26]  Zhuowen Tu,et al.  Combining Generative and Discriminative Models for Semantic Segmentation of CT Scans via Active Learning , 2011, IPMI.

[27]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[28]  S. Lehéricy,et al.  Cholinergic neuronal loss in the globus pallidus of Alzheimer disease patients , 1991, Neuroscience Letters.

[29]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[30]  for the Alzheimer's Disease Neuroimaging Initiative,et al.  Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer’s disease patients: From the alzheimer’s disease neuroimaging initiative (ADNI) database , 2017, Journal of Neuroscience Methods.

[31]  for the Alzheimer's Disease Neuroimaging Initiative,et al.  Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares , 2017, Journal of Neuroscience Methods.

[32]  Giorgio Roffo,et al.  Feature Selection Library (MATLAB Toolbox) , 2016, 1607.01327.

[33]  R. Grebe,et al.  Influence of age and osteoporosis on calcaneus trabecular bone structure: a preliminary in vivo MRI study by quantitative texture analysis. , 2004, Magnetic resonance imaging.

[34]  Sylvia Drabycz,et al.  An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging , 2010, NeuroImage.

[35]  Andrés Ortiz,et al.  Evaluating Alzheimer's Disease Diagnosis Using Texture Analysis , 2017, MIUA.

[36]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[37]  David G. Stork,et al.  Pattern Classification , 1973 .

[38]  Krzysztof Michalak,et al.  Correlation based feature selection method , 2010, Int. J. Bio Inspired Comput..

[39]  J. M. GÓRRIZ,et al.  Case-Based Statistical Learning: A Non-Parametric Implementation With a Conditional-Error Rate SVM , 2017, IEEE Access.

[40]  Heung-Il Suk,et al.  Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.

[41]  Johannes Pantel,et al.  Structural Changes of the Corpus Callosum in Mild Cognitive Impairment and Alzheimer’s Disease , 2006, Dementia and Geriatric Cognitive Disorders.

[42]  Taku Komura,et al.  Automatic Irregular Texture Detection in Brain MRI Without Human Supervision , 2018, MICCAI.

[43]  Elsa D. Angelini,et al.  Alzheimer's disease diagnosis based on anatomically stratified texture analysis of the hippocampus in structural MRI , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[44]  Yi Pan,et al.  Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[45]  Benson Mwangi,et al.  A Review of Feature Reduction Techniques in Neuroimaging , 2013, Neuroinformatics.

[46]  A. Kassner,et al.  Texture Analysis: A Review of Neurologic MR Imaging Applications , 2010, American Journal of Neuroradiology.

[47]  Nathalie Japkowicz,et al.  The Class Imbalance Problem: Significance and Strategies , 2000 .

[48]  Hao Wang,et al.  Markov Blanket Feature Selection Using Representative Sets , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[49]  Justin C. W. Debuse,et al.  Feature Subset Selection within a Simulated Annealing Data Mining Algorithm , 1997, Journal of Intelligent Information Systems.

[50]  Chunshui Yu,et al.  3D texture analysis on MRI images of Alzheimer’s disease , 2011, Brain Imaging and Behavior.

[51]  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 .

[52]  Pierrick Coupé,et al.  Adaptive fusion of texture-based grading for Alzheimer's disease classification , 2018, Comput. Medical Imaging Graph..

[53]  Volker Roth,et al.  The generalized LASSO , 2004, IEEE Transactions on Neural Networks.

[54]  Alzheimer’s Association.  2018 Alzheimer's disease facts and figures , 2018, Alzheimer's & Dementia.

[55]  Chokri Ben Amar,et al.  Recognition of Alzheimer's disease and Mild Cognitive Impairment with multimodal image-derived biomarkers and Multiple Kernel Learning , 2017, Neurocomputing.

[56]  N. Amoroso,et al.  DTI measurements for Alzheimer’s classification , 2017, Physics in medicine and biology.

[57]  S. Rose,et al.  Gray and white matter changes in Alzheimer's disease: A diffusion tensor imaging study , 2008, Journal of magnetic resonance imaging : JMRI.

[58]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[59]  Ewert Bengtsson,et al.  3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading , 2014, Comput. Math. Methods Medicine.

[60]  Bin Hu,et al.  Alzheimer’s Disease Classification Based on Individual Hierarchical Networks Constructed With 3-D Texture Features , 2017, IEEE Transactions on NanoBioscience.

[61]  Sara van de Geer,et al.  Statistics for High-Dimensional Data: Methods, Theory and Applications , 2011 .

[62]  Hyunjin Park,et al.  Structural and functional connectional fingerprints in mild cognitive impairment and Alzheimer’s disease patients , 2017, PloS one.

[63]  G. Paxinos,et al.  Atlas of the Human Brain , 2000 .

[64]  Yunyan Zhang,et al.  MRI Texture Analysis in Multiple Sclerosis , 2011, Int. J. Biomed. Imaging.

[65]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[66]  Yi Ding,et al.  Brain MR Image Classification for Alzheimer's Disease Diagnosis Based on Multifeature Fusion , 2017, Comput. Math. Methods Medicine.

[67]  Asim M. Mubeen,et al.  Prediction of Incipient Alzheimer's Disease Dementia in Patients with Mild Cognitive Impairment. , 2016, Journal of Alzheimer's disease : JAD.

[68]  R. Athilakshmi,et al.  Feature Selection Techniques for Prediction of Neuro-Degenerative Disorders: A Case-Study with Alzheimer’s And Parkinson’s Disease , 2017 .

[69]  Henrik Zetterberg,et al.  Alzheimer's disease markers, hypertension, and gray matter damage in normal elderly , 2012, Neurobiology of Aging.

[70]  Yuanyuan Ding,et al.  Improving the Performance of SVM-RFE to Select Genes in Microarray Data , 2006, BMC Bioinformatics.

[71]  C. Caltagirone,et al.  Regional brain atrophy and functional disconnection across Alzheimer's disease evolution , 2010, Journal of Neurology, Neurosurgery & Psychiatry.

[72]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[73]  A. Mechelli,et al.  Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications , 2017, Neuroscience & Biobehavioral Reviews.

[74]  Ruihu Wang,et al.  AdaBoost for Feature Selection, Classification and Its Relation with SVM, A Review , 2012 .

[75]  Eduardo Romero,et al.  Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease , 2018, Brain and behavior.

[76]  Seong Jin Cho,et al.  Voxel-based analysis of Alzheimer's disease PET imaging using a triplet of radiotracers: PIB, FDDNP, and FDG , 2010, NeuroImage.

[77]  Pablo A. Estévez,et al.  A review of feature selection methods based on mutual information , 2013, Neural Computing and Applications.

[78]  Rich Caruana,et al.  Greedy Attribute Selection , 1994, ICML.

[79]  Daniel Rueckert,et al.  Random forest-based similarity measures for multi-modal classification of Alzheimer's disease , 2013, NeuroImage.

[80]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[81]  Guy Marchal,et al.  Automated multi-moda lity image registration based on information theory , 1995 .

[82]  B. Miller,et al.  Distinct MRI Atrophy Patterns in Autopsy-Proven Alzheimer's Disease and Frontotemporal Lobar Degeneration , 2008, American journal of Alzheimer's disease and other dementias.