View-centralized multi-atlas classification for Alzheimer's disease diagnosis

Human Brain Mapping

Volumn 36, Issue 5, 2015, Pages 1847-1865

  Liu, Mingxia ^a,b,c   Zhang, Daoqiang ^a   Shen, Dinggang ^b,d  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^c TAISHAN UNIVERSITY   (China)

^d Korea University   (South Korea)

Author keywords

Alzheimer's disease; Ensemble learning; Feature selection; Multiatlas classification; Multiview learning

Indexed keywords

ALGORITHM; ALZHEIMER DISEASE; ARTICLE; BRAIN MAPPING; CLINICAL ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC PROCEDURE; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE CLASSIFICATION; HUMAN; IMAGE ANALYSIS; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; VIEW CENTRALIZED MULTI ATLAS CLASSIFICATION; AGED; BOOK; BRAIN; COMPUTER ASSISTED DIAGNOSIS; EVALUATION STUDY; FACTUAL DATABASE; FEMALE; MALE; PATHOLOGY; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY;

AGED; ALZHEIMER DISEASE; ATLASES AS TOPIC; BRAIN; DATABASES, FACTUAL; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MILD COGNITIVE IMPAIRMENT; ROC CURVE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

EID: 84927690405     PISSN: 10659471     EISSN: 10970193     Source Type: Journal    
DOI: 10.1002/hbm.22741     Document Type: Article

References (79)

1. Ashburner J, Friston KJ (2000): Voxel-based morphometry-the methods. NeuroImage 11:805-821.
2. Ashburner J, Hutton C, Frackowiak R, Johnsrude I, Price C, Friston K (1998): Identifying global anatomical differences: Deformation-based morphometry. Hum Brain Mapp 6:348-357.
3. Basha T, Moses Y, Kiryati N (2013): Multi-view scene flow estimation: A view centered variational approach. Int J Comput Vis 101:6-21.
4. Beck A, Teboulle M (2009): A fast iterative shrinkage-thresholding algorithm for linear inverse problems. Siam J Imging Sci 2:183-202.
5. Bozzali M, Filippi M, Magnani G, Cercignani M, Franceschi M, Schiatti E, Castiglioni S, Mossini R, Falautano M, Scotti, G, Comi G, Falini A. (2006): The contribution of voxel-based morphometry in staging patients with mild cognitive impairment. Neurology 67:453-460.
6. Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM (2007): Forecasting the global burden of Alzheimer's disease. Alzheimers Dement 3:186-191.
7. Burges CJ (1998): A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121-167.
8. Chan D, Janssen JC, Whitwell JL, Watt HC, Jenkins R, Frost C, Rossor MN, Fox NC (2003): Change in rates of cerebral atrophy over time in early-onset Alzheimer's disease: Longitudinal MRI study. Lancet 362:1121-1122.
9. Chang C-C, Lin C-J(2011): LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2:27.
10. Chen X, Pan W, Kwok JT, Carbonell JG (2009a): Accelerated gradient method for multitask sparse learning problem. In Proceedings of IEEE International Conference on Data Mining. Florida, USA.
11. Chen Y, An H, Zhu H, Stone T, Smith JK, Hall C, Bullitt E, Shen D, Lin W (2009b): White matter abnormalities revealed by diffusion tensor imaging in non-demented and demented HIV+ patients. NeuroImage 47:1154-1162.
12. Chung M, Worsley K, Paus T, Cherif C, Collins D, Giedd J, Rapoport J, Evans A (2001): A unified statistical approach to deformation-based morphometry. NeuroImage 14:595-606.
13. Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert, M.-O., Chupin M, Benali H, Colliot O (2011): Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database. NeuroImage 56:766-781.
14. Davatzikos C, Genc A, Xu D, Resnick SM (2001): Voxel-based morphometry using the RAVENS maps: Methods and validation using simulated longitudinal atrophy. NeuroImage 14:1361-1369.
15. Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM (2008): Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 29:514-523.
16. Dennis JJE, Schnabel RB (1983): Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Inc, Englewood Cliffs, NJ.
17. Dickerson BC, Goncharova I, Sullivan M, Forchetti C, Wilson R, Bennett D, Beckett L, deToledo-Morrell L (2001): MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer's disease. Neurobiol Aging 22:747-754.
18. Fan Y, Shen D, Gur RC, Gur RE, Davatzikos C (2007): COMPARE: Classification of morphological patterns using adaptive regional elements. IEEE Trans Med Imaging 26:93-105.
19. Fan Y, Resnick SM, Wu X, Davatzikos C (2008): Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study. NeuroImage 41:277-285.
20. Fox N, Warrington E, Freeborough P, Hartikainen P, Kennedy A, Stevens J, Rossor MN (1996): Presymptomatic hippocampal atrophy in Alzheimer's disease A longitudinal MRI study. Brain 119:2001-2007.
21. Frey BJ, Dueck D (2007): Clustering by passing messages between data points. Science 315:972-976.
22. Frisoni G, Testa C, Zorzan A, Sabattoli F, Beltramello A, Soininen H, Laakso M (2002): Detection of grey matter loss in mild Alzheimer's disease with voxel based morphometry. J Neurol Neurosur Ps 73:657-664.
23. Gaser C, Nenadic I, Buchsbaum BR, Hazlett EA, Buchsbaum MS (2001): Deformation-based morphometry and its relation to conventional volumetry of brain lateral ventricles in MRI. NeuroImage 13:1140-1145.
24. Goldszal AF, Davatzikos C, Pham DL, Yan MX, Bryan RN, Resnick SM (1998): An image-processing system for qualitative and quantitative volumetric analysis of brain images. J Comput Assist Tomo 22:827-837.
25. Gong Y, Ke Q, Isard M, Lazebnik S (2014): A multi-view embedding space for modeling internet images, tags, and their semantics. Int J Comput Vis 106:210-233.
26. Grau V, Mewes A, Alcaniz M, Kikinis R, Warfield SK (2004): Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 23:447-458.
27. Guyon I, Weston J, Barnhill S, Vapnik V (2002): Gene selection for cancer classification using support vector machines. Mach Learn 46:389-422.
28. Hinrichs C, Singh V, Mukherjee L, Xu G, Chung MK, Johnson SC (2009): Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. NeuroImage 48:138-149.
29. Ho TK (1998): The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832-844.
30. Hua X, Hibar DP, Ching CR, Boyle CP, Rajagopalan P, Gutman BA, Leow AD, Toga AW, Jack CR, Jr, Harvey D, Weiner Michael W, Thompson Paul M. (2013): Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials. NeuroImage 66:648-661.
31. Jack CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ, L Whitwell J, Ward C, Dale AM, Felmlee JP, Gunter JL, Hill DLG, Killiany R, Schuff N, Fox-Bosetti D, Lin C, Studholme C, DeCarli CS, Krueger G, Ward HA, Metzger GJ, Scott KT, Mallozzi R, Blezek D, Levy J, Debbins JP, Fleisher AS, Albert M, Green R, Bartzokis G, Glover Gary, Mugler J, Weiner MW. (2008): The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 27:685-691.
32. Jenkinson M, Smith S (2001): A global optimisation method for robust affine registration of brain images. Med Image Anal 5:143-156.
33. Jenkinson M, Bannister P, Brady M, Smith S (2002): Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17:825-841.
34. Jie B, Zhang D, Wee CY, Shen D (2014): Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Hum Brain Mapp 35:2876-2897.
35. Joseph J, Warton C, Jacobson SW, Jacobson JL, Molteno CD, Eicher A, Marais P, Phillips OR, Narr KL, Meintjes EM (2014): Three-dimensional surface deformation-based shape analysis of hippocampus and caudate nucleus in children with fetal alcohol spectrum disorders. Hum Brain Mapp 35:659-672.
36. Kipps C, Duggins A, Mahant N, Gomes L, Ashburner J, McCusker E (2005): Progression of structural neuropathology in preclinical Huntington's disease: A tensor based morphometry study. J Neurol Neurosurg Psychiatry 76:650-655.
37. Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, Fox NC, Jack CR, Ashburner J, Frackowiak RS (2008): Automatic classification of MR scans in Alzheimer's disease. Brain 131:681-689.
38. Koikkalainen J, Lötjönen J, Thurfjell L, Rueckert D, Waldemar G, Soininen H (2011): Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease. NeuroImage 56:1134-1144.
39. Leow AD, Klunder AD, Jack CR, Jr, Toga AW, Dale AM, Bernstein MA, Britson PJ, Gunter JL, Ward CP, Whitwell JL (2006): Longitudinal stability of MRI for mapping brain change using tensor-based morphometry. NeuroImage 31:627-640.
40. Leporé N, Brun C, Chou, Y.-Y., Lee A, Barysheva M, De Zubicaray GI, Meredith M, Macmahon K, Wright M, Toga AW (2008): Multi-atlas tensor-based morphometry and its application to a genetic study of 92 twins. In Proceedings of Medical Image Computing and Computer-Assisted Intervention Workshop on Mathematical Foundations of Computational Anatomy. New York, USA, 6-10 September, 2008.
41. Li SZ, Zhu L, Zhang Z, Blake A, Zhang H, Shum H (2002): Statistical learning of multi-view face detection. In Proceedings of European Conference on Computer Vision. Copenhagen, Danmark, Springer, 27 May 2002.
42. Li Y, Wang Y, Wu G, Shi F, Zhou L, Lin W, Shen D (2012): Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features. Neurobiology of Aging 33:427. e15-427. e30.
43. Liu M, Zhang D, Shen D (2012): Ensemble sparse classification of Alzheimer's disease. NeuroImage 60:1106-1116.
44. Liu J, Ye, J 2009. Efficient L1/Lq Norm Regularization. Arizona State University, Phoenix, AZ, USA.
45. Liu M, Zhang D, Shen D (2012): Ensemble sparse classification of Alzheimer's disease. NeuroImage 60:1106-1116.
46. Liu M, Zhang D, Shen D (2013): Identifying informative imaging biomarkers via tree structured sparse learning for AD diagnosis. Neuroinformatics 12:381-394.
47. Liu M, Zhang D, Shen D (2014): Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis. Hum Brain Mapp 35:1305-1319.
48. Magnin B, Mesrob L, Kinkingnéhun S, Pélégrini-Issac M, Colliot O, Sarazin M, Dubois B, Lehéricy S, Benali H (2009): Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI. Neuroradiology 51:73-83.
49. Min R, Wu G, Cheng J, Wang Q, Shen D (2014a): Multi-atlas based representations for Alzheimer's disease diagnosis. Hum Brain Mapp 35:5052-5070. DOI: 10.1002/hbm.22531.
50. Min R, Wu G, Shen D (2014b): Maximum-margin based representation learning from multiple atlases for Alzheimer's disease classication. Medical Image Computing and Computer-Assisted Intervention. Boston, USA.
51. Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W, Trojanowski JQ, Toga AW, Beckett L (2005): Ways toward an early diagnosis in Alzheimer's disease: The Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimers Dement 1:55-66.
52. Pereira F, Mitchell T, Botvinick M (2009): Machine learning classifiers and fMRI: A tutorial overview. NeuroImage 45:S199-S209.
53. Pluim JP, Maintz JA, Viergever MA (2003): Mutual-information-based registration of medical images: A survey. IEEE Trans Med Imaging 22:986-1004.
54. Rodriguez JJ, Kuncheva LI, Alonso CJ (2006): Rotation forest: A new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28:1619-1630.
55. Shen D, Davatzikos C (2002): HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Trans Med Imaging 21:1421-1439.
56. Shi F, Yap P-T, Gao W, Lin W, Gilmore JH, Shen D (2012): Altered structural connectivity in neonates at genetic risk for schizophrenia: a combined study using morphological and white matter networks. Neuroimage 62:1622-1633.
57. Sled JG, Zijdenbos AP, Evans AC (1998): A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87-97.
58. Sotiras A, Davatzikos C, Paragios N (2013): Deformable medical image registration: A survey. IEEE Trans Med Imaging 32:1153-1190.
59. Suk H-I, Lee S-W, Shen D (2013): Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct 1-19.
60. Tang S, Fan Y, Wu G, Kim M, Shen D (2009): RABBIT: Rapid alignment of brains by building intermediate templates. NeuroImage 47:1277-1287.
61. Teipel SJ, Born C, Ewers M, Bokde AL, Reiser MF, Möller H-J, Hampel H (2007): Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment. NeuroImage 38:13-24.
62. Thomas A, Ferrar V, Leibe B, Tuytelaars T, Schiel B, Van Gool L (2006): Towards multi-view object class detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. New York, USA, IEEE Computer Society, 17-22 June 2006.
63. Thompson PM, Mega MS, Woods RP, Zoumalan CI, Lindshield CJ, Blanton RE, Moussai J, Holmes CJ, Cummings JL, Toga AW (2001): Cortical change in Alzheimer's disease detected with a disease-specific population-based brain atlas. Cerebral Cortex 11:1-16.
64. Tibshirani R (1996): Regression shrinkage and selection via the lasso. J R Stat Soc B 58:267-288.
65. Vemuri P, Gunter JL, Senjem ML, Whitwell JL, Kantarci K, Knopman DS, Boeve BF, Petersen RC, Jack CR, Jr (2008): Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies. NeuroImage 39:1186-1197.
66. Vincent L, Soille P (1991): Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13:583-598.
67. Wang Y, Nie J, Yap P-T, Li G, Shi F, Geng X, Guo L, Shen D, for the Alzheimer's Disease Neuroimaging Initiative (2014): Knowledge-guided robust MRI brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates. PloS one 9:e77810.
68. Wee CY, Yap P-T, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, Shen D (2012): Resting-state multi-spectrum functional connectivity networks for identification of MCI patients. PloS one 7:e37828.
69. Wee CY, Yap P-T, Shen D (2013): Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns. Human Brain Mapping 34:3411-3425.
70. Wee CY, Yap P-T, Zhang D, Wang L, Shen D (2014): Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification. Brain Structure and Function 219:641-656.
71. Whitford TJ, Grieve SM, Farrow TF, Gomes L, Brennan J, Harris AW, Gordon E, Williams LM (2006): Progressive grey matter atrophy over the first 2-3 years of illness in first-episode schizophrenia: A tensor-based morphometry study. NeuroImage 32:511-519.
72. Wolz R, Julkunen V, Koikkalainen J, Niskanen E, Zhang DP, Rueckert D, Soininen H, Lötjönen J, for the Alzheimer's Disease Neuroimaging Initiative (2011): Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease. PloS one 6:e25446.
73. Xu C, Tao D, Xu C (2014): Large-margin multi-view information bottleneck. IEEE Trans Pattern Anal Mach Intell 36:1159-1572.
74. Yap P-T, Wu G, Zhu H, Lin W, Shen D (2009): TIMER: Tensor image morphing for elastic registration. NeuroImage 47:549-563.
75. Zhang D, Shen D (2012): Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. NeuroImage 59:895-907.
76. Zhang D, Shen D, Initiative AsDN (2012): Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PloS one 7:e33182.
77. Zhang D, Wang Y, Zhou L, Yuan H, Shen D (2011): Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage 55:856-867.
78. Zhang Y, Brady M, Smith S (2001): Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20:45-57.
79. Zou H, Hastie T (2005): Regularization and variable selection via the elastic net. J R Stat Soc B 67:301-320.