Spatial and anatomical regularization of SVM: A general framework for neuroimaging data

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 35, Issue 3, 2013, Pages 682-696

  Cuingnet, Rémi ^a   Glaunés, Joan Alexis ^b,c,d,e,f   Chupin, Marie ^c,d,e,f,g   Benali, Habib ^h   Colliot, Olivier ^c,d,e,f,g  

^a PHILIPS RESEARCH LABORATORIES   (Germany)

^b UNIVERSITÉ PARIS DESCARTES   (France)

^c SORBONNE UNIVERSITÉ   (France)

^d INSERM   (France)

^e CNRS   (France)

^f INSTITUT DU CERVEAU ET DE LA MOELLE ÉPINIÈRE   (France)

^g Centre National de la Recherche Scientifique   (France)

^h UPMC Univ Paris 06   (France)

Author keywords

Alzheimer's disease; Laplacian; neuroimaging; regularization; SVM

Indexed keywords

ALZHEIMER'S DISEASE; ANATOMICAL PRIORS; BRAIN CONNECTIVITY; BRAIN IMAGE ANALYSIS; CLASSIFICATION FUNCTIONS; CLASSIFICATION PERFORMANCE; CONCENTRATION MAPS; CORTICAL THICKNESS; EFFICIENT COMPUTATION; FEATURE MAP; GRAM MATRICES; GRAPH LAPLACIAN; GRAY MATTER; HEAT KERNEL; HIGH FREQUENCY COMPONENTS; IMAGE POINTS; LAPLACE-BELTRAMI OPERATOR; LAPLACIANS; REGULARIZATION; STATISTICAL MANIFOLDS; SVM; SVM-OPTIMIZATION;

LAPLACE TRANSFORMS; MAGNETIC RESONANCE; NEUROIMAGING;

BRAIN;

AGED; ALZHEIMER DISEASE; ANATOMY AND HISTOLOGY; BRAIN; FEMALE; HUMAN; IMAGE PROCESSING; MALE; MIDDLE AGED; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES; SUPPORT VECTOR MACHINE; VERY ELDERLY;

AGED; AGED, 80 AND OVER; ALZHEIMER DISEASE; BRAIN; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; NEUROIMAGING; SUPPORT VECTOR MACHINE;

EID: 84874508003     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2012.142     Document Type: Article

References (61)

1. V.N. Vapnik, The Nature of Statistical Learning Theory. Springer-Verlag, 1995.
2. B. Schölkopf and A.J. Smola, Learning with Kernels. MIT Press, 2001.
3. J. Shawe-Taylor and N. Cristianini, Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge Univ. Press, 2000.
4. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004.
5. S. Duchesne, A. Caroli, C. Geroldi, C. Barillot, G. Frisoni, and D. Collins, "MRI-Based Automated Computer Classification of Probable AD versus Normal Controls," IEEE Trans. Medical Imaging, vol. 27, no. 4, pp. 509-20, Apr. 2008. (Pubitemid 351479143)
6. C. Davatzikos, S. Resnick, X. Wu, P. Parmpi, and C. Clark, "Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI," NeuroImage, vol. 41, no. 4, pp. 1220-1227, 2008.
7. S. Klöppel, C.M. Stonnington, C. Chu, B. Draganski, R.I. Scahill, J.D. Rohrer, N.C. Fox, C.R. Jack, J. Ashburner, and R.S.J. Frackowiak, "Automatic Classification of MR Scans in Alzheimer's Disease," Brain, vol. 131, no. 3, pp. 681-689, 2008a. (Pubitemid 351294712)
8. P. Vemuri et al., "Alzheimer's Disease Diagnosis in Individual Subjects Using Structural MR Images: Validation Studies," Neuro-Image, vol. 39, no. 3, pp. 1186-1197, 2008.
9. E. Gerardin et al., "Multidimensional Classification of Hippocampal Shape Features Discriminates Alzheimer's Disease and Mild Cognitive Impairment from Normal Aging," NeuroImage, vol. 47, no. 4, pp. 1476-1486, 2009.
10. R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, M.-O. Habert, M. Chupin, H. Benali, and O. Colliot, "Automatic Classification of Patients with Alzheimer's Disease from Structural MRI: A Comparison of ten Methods Using the ADNI Database," NeuroImage, vol. 56, no. 2, pp. 766-781, 2011.
11. S. Klöppel et al., "Accuracy of Dementia Diagnosis-A Direct Comparison between Radiologists and a Computerized Method," Brain, vol. 131, no. 11, pp. 2969-2974, 2008b.
12. C. Ecker et al., "Investigating the Predictive Value of Whole-Brain Structural MR Scans in Autism: A Pattern Classification Approach," NeuroImage, vol. 49, no. 1, pp. 44-56, 2010.
13. Y. Fan, D. Shen, R.C. Gur, R.E. Gur, and C. Davatzikos, "COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements," IEEE Trans. Medical Imaging, vol. 26, no. 1, pp. 93-105, Jan. 2007. (Pubitemid 46096354)
14. S. Duchesne, Y. Rolland, and M. Vérin, "Automated Computer Differential Classification in Parkinsonian Syndromes via Pattern Analysis on MRI," Academic Radiology, vol. 16, no. 1, pp. 61-70, 2009.
15. C. Hinrichs, V. Singh, L. Mukherjee, G. Xu, M.K. Chung, S.C. Johnson, and the ADNI, "Spatially Augmented LPboosting for AD Classification with Evaluations on the ADNI Dataset," Neuro-Image, vol. 48, no. 1, pp. 138-149, 2009.
16. T. Gärtner, "A Survey of Kernels for Structured Data," ACM SIGKDD Explorations Newsletter, vol. 5, no. 1, pp. 49-58, 2003.
17. T. Jaakkola and D. Haussler, "Exploiting Generative Models in Discriminative Classifiers," Proc. Conf. Advances in Neural Information Processing Systems, pp. 487-493, 1999.
18. B. Schö lkopf, P. Simard, A. Smola, and V. Vapnik, "Prior Knowledge in Support Vector Kernels," Proc. Conf. Advances in Neural Information Processing Systems, pp. 640-646, 1998.
19. B. Schölkopf, C. Burges, and V. Vapnik, "Incorporating Invariances in Support Vector Learning Machines," Proc. Int'l Conf. Artificial Neural Networks, pp. 47-52, 1996. (Pubitemid 126098507)
20. D. Decoste and B. Schölkopf, "Training Invariant Support Vector Machines," Machine Learning, vol. 46, no. 1, pp. 161-190, 2002. (Pubitemid 34129967)
21. A.J. Smola and B. Schölkopf, "On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion," Algorithmica, vol. 22, no. 1/2, pp. 211-231, 1998. (Pubitemid 128684257)
22. F. Rapaport, A. Zinovyev, M. Dutreix, E. Barillot, and J.-P. Vert, "Classification of Microarray Data Using Gene Networks," BMC Bioinformatics, vol. 8, no. 1, p. 35, 2007.
23. R. Cuingnet, C. Rosso, M. Chupin, S. Lehéricy, D. Dormont, H. Benali, Y. Samson, and O. Colliot, "Spatial Regularization of SVM for the Detection of Diffusion Alterations Associated with Stroke Outcome," Medical Image Analysis, vol. 15, pp. 729-737, 2011.
24. S. Geman, D. Geman, and S. Relaxation, "Gibbs Distributions, and the Bayesian Restoration of Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 2, pp. 721-741, Nov. 1984. (Pubitemid 15453722)
25. I. Bloch, O. Colliot, O. Camara, and T. Géraud, "Fusion of Spatial Relationships for Guiding Recognition, Example of Brain Structure Recognition in 3D MRI," Pattern Recognition Letters, vol. 26, no. 4, pp. 449-57, 2005. (Pubitemid 40220140)
26. P. Hagmann, L. Cammoun, X. Gigandet, R. Meuli, C. Honey, V. Wedeen, and O. Sporns, "Mapping the Structural Core of Human Cerebral Cortex," PLoS Biology, vol. 6, no. 7, p. e159, 2008.
27. R. Cuingnet, M. Chupin, H. Benali, and O. Colliot, "Spatial and Anatomical Regularization of SVM for Brain Image Analysis," Proc. Conf. Neural Information Processing Systems, pp. 460-468, 2010.
28. R. Cuingnet, J.A. Glaunès, M. Chupin, H. Benali, and O. Colliot, "Anatomical Regularizations on Statistical Manifolds for the Classification of Patients with Alzheimer's Disease," Proc. Workshop Machine Learning in Medical Imaging, pp. 201-208, 2011.
29. D. Shen and C. Davatzikos, "HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration." IEEE Trans. Medical Imaging, vol. 21, no. 11, pp. 1421-1439, Nov. 2002.
30. J. Ashburner, "A Fast Diffeomorphic Image Registration Algorithm," NeuroImage, vol. 38, no. 1, pp. 95-113, 2007. (Pubitemid 47440133)
31. Z. Lao, D. Shen, Z. Xue, B. Karacali, S.M. Resnick, and C. Davatzikos, "Morphological Classification of Brains via High-Dimensional Shape Transformations and Machine Learning Methods," NeuroImage, vol. 21, no. 1, pp. 46-57, 2004. (Pubitemid 38114995)
32. O. Querbes, F. Aubry, J. Pariente, J.-A. Lotterie, J.-F. Demonet, V. Duret, M. Puel, I. Berry, J.-C. Fort, P. Celsis and the ADNI, "Early Diagnosis of Alzheimer's Disease Using Cortical Thickness: Impact of Cognitive Reserve," Brain, vol. 132, no. 8, pp. 2036-2047, 2009.
33. U. Von Luxburg, "A Tutorial on Spectral Clustering," Statistics and Computing, vol. 17, no. 4, pp. 395-416, 2007.
34. J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
35. R.I. Kondor and J.D. Lafferty, "Diffusion Kernels on Graphs and Other Discrete Input Spaces," Proc. Int'l Conf. Machine Learning, pp. 315-322, 2002.
36. L. Gomez, G. Camps-Valls, J. Muñoz-Maŕ, and J. Calpe-Maravilla, "Semisupervised Image Classification with Laplacian Support Vector Machines," IEEE Geoscience and Remote Sensing Letters, vol. 5, no. 3, pp. 336-340, July 2008.
37. A. Smola and R. Kondor, "Kernels and Regularization on Graphs," Proc. 16th Ann. Conf. Computational Learning Theory, pp. 144-158, 2003. (Pubitemid 37053201)
38. J. Jost, Riemannian Geometry and Geometric Analysis. Springer, 2008.
39. J. Lafferty and G. Lebanon, "Diffusion Kernels on Statistical Manifolds," J. Machine Learning Research, vol. 6, pp. 129-63, 2005.
40. E. Hebey, Sobolev Spaces on Riemannian Manifolds. Springer, 1996.
41. A. Andrade, F. Kherif, J. Mangin, K. Worsley, A. Paradis, O. Simon, S. Dehaene, D. Le Bihan, and J. Poline, "Detection of fMRI Activation Using Cortical Surface Mapping," Human Brain Mapping, vol. 12, no. 2, pp. 79-93, 2001. (Pubitemid 32120706)
42. A. Cachia et al., "A Primal Sketch of the Cortex Mean Curvature: A Morphogenesis Based Approach to Study the Variability of the Folding Patterns," IEEE Trans. Medical Imaging, vol. 22, no. 6, pp. 754-765, June 2003.
43. M.K. Chung, S. Robbins, and A.C. Evans, "Unified Statistical Approach to Cortical Thickness Analysis," Information Processing in Medical Imaging, vol. 3565, pp. 627-638, 2005. (Pubitemid 41425351)
44. S. Rosenberg, The Laplacian on a Riemannian Manifold: An Introduction to Analysis on Manifolds. Cambridge Univ. Press, 1997.
45. G. Golub and C. Van Loan, Matrix Computations. Johns Hopkins Univ. Press, 1996.
46. S.-I. Amari, O.E. Barndorff-Nielsen, R.E. Kass, S.L. Lauritzen, and C.R. Rao, Differential Geometry in Statistical Inference, vol. 1., Inst. of Math. Statistics, 1987.
47. O. Druet, E. Hebey, and F. Robert, Blow-Up Theory for Elliptic PDEs in Riemannian Geometry. Princeton Univ Press, 2004.
48. C.D. Good, I.S. Johnsrude, J. Ashburner, R.N. Henson, K.J. Friston, and R.S. Frackowiak, "A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains," NeuroImage, vol. 14, no. 1, pp. 21-36, 2001. (Pubitemid 32552740)
49. J.L. Whitwell et al., "MRI Patterns of Atrophy Associated with Progression to AD in Amnestic Mild Cognitive Impairment," Neurology, vol. 70, no. 7, pp. 512-520, 2008. (Pubitemid 351653375)
50. P.M. Thompson et al., "Mapping Cortical Change in Alzheimer's Disease, Brain Development, and Schizophrenia," NeuroImage, vol. 23, no. Suppl 1, pp. S2-S18, 2004. (Pubitemid 39421703)
51. J.P. Lerch, J.C. Pruessner, A.P. Zijdenbos, D.L. Collins, S.J. Teipel, H. Hampel, and A.C. Evans, "Automated Cortical Thickness Measurements from MRI Can Accurately Separate Alzheimer's Patients from Normal Elderly Controls," Neurobiology of Aging, vol. 29, no. 1, pp. 23-30, 2008. (Pubitemid 350110605)
52. Y. Fan, N. Batmanghelich, C. Clark, C. Davatzikos and the ADNI, "Spatial Patterns of Brain Atrophy in MCI Patients, Identified via High-Dimensional Pattern Classification, Predict Subsequent Cognitive Decline," NeuroImage, vol. 39, no. 4, pp. 1731-1743, 2008. (Pubitemid 351179414)
53. O. Colliot, G. Chételat, M. Chupin, B. Desgranges, B. Magnin, H. Benali, B. Dubois, L. Garnero, F. Eustache, and S. Lehéricy, "Discrimination between Alzheimer Disease, Mild Cognitive Impairment, and Normal Aging by Using Automated Segmentation of the Hippocampus," Radiology, vol. 248, no. 1, pp. 194-201, 2008.
54. M. Chupin, E. Gerardin, R. Cuingnet, C. Boutet, L. Lemieux, S. Lehéricy, H. Benali, L. Garnero, O. Colliot, and the ADNI, "Fully Automatic Hippocampus Segmentation and Classification in Alzheimer's Disease and Mild Cognitive Impairment Applied on Data from ADNI," Hippocampus, vol. 19, no. 6, pp. 579-587, 2009.
55. C.R. Jack et al., "The Alzheimer's Disease Neuroimaging (ADNI): MRI Methods," J. Magnetic Resonance Imaging, vol. 27, no. 4, pp. 685-691, 2008.
56. J. Ashburner and K.J. Friston, "Unified Segmentation," Neuro-Image, vol. 26, no. 3, pp. 839-851, 2005. (Pubitemid 40825026)
57. B. Fischl, M.I. Sereno, R.B.H. Tootell, and A.M. Dale, "High-Resolution Inter-Subject Averaging and a Coordinate System for the Cortical Surface," Human Brain Mapping, vol. 8, pp. 272-284, 1999.
58. B. Fischl and A.M. Dale, "Measuring the Thickness of the Human Cerebral Cortex from Magnetic Resonance Images," Proc. Nat'l Academy of Sciences USA, no. 97, pp. 11044-11049, 2000.
59. N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, "Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain," NeuroImage, vol. 15, no. 1, pp. 273-289, 2002. (Pubitemid 34852226)
60. R.S. Desikan et al., "An Automated Labeling System for Subdividing the Human Cerebral Cortex on MRI Scans into Gyral Based Regions of Interest," NeuroImage, vol. 31, no. 3, pp. 968-980, 2006. (Pubitemid 44183792)
61. G.F. Busatto et al., "A Voxel-Based Morphometry Study of Temporal Lobe Gray Matter Reductions in Alzheimer's Disease," Neurobiology of Aging, vol. 24, no. 2, pp. 221-231, 2003. (Pubitemid 36005403)