Feature-based morphometry: Discovering group-related anatomical patterns

NeuroImage

Volumn 49, Issue 3, 2010, Pages 2318-2327

  Toews, Matthew ^a   Wells III, William ^a,b   Collins, D Louis ^c   Arbel, Tal ^d  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c MCGILL UNIVERSITY   (Canada)

^d MCGILL UNIVERSITY   (Canada)

Author keywords

Alzheimer's disease; Brain image analysis; Computer aided diagnosis; Group differences; Image biomarkers; Machine learning; Morphometry; Scale invariant features

Indexed keywords

BIOLOGICAL MARKER;

ADULT; AGED; ALZHEIMER DISEASE; ANATOMICAL VARIATION; ARTICLE; CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; DATA BASE; DISEASE SEVERITY; FEATURE BASED MORPHOMETRY; GEOMETRY; HUMAN; IMAGE ANALYSIS; LEARNING; MAJOR CLINICAL STUDY; MORPHOMETRICS; PRIORITY JOURNAL; PROBABILITY; STATISTICAL MODEL; THEORY;

AGED; ALZHEIMER DISEASE; BRAIN; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED;

EID: 71849112376     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2009.10.032     Document Type: Article

References (57)

1. Allaire, S., Kim, J., Breen, S., Jaffray, D., Pekar, V., 2008. Full orientation invariance and improved feature selectivity of 3d sift with application to medical image analysis. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis.
2. Ashburner J., and Friston K.J. Voxel-based morphometry-the methods. NeuroImage 11 23 (2000) 805-821
3. Ashburner J., and Friston K.J. Comments and controversies: why voxel-based morphometry should be used. NeuroImage 14 (2001) 1238-1243
4. Ashburner J., Hutton C., Frackowiak R., Johnsrude I., Price C., and Friston K. Identifying global anatomical differences: deformation-based morphometry. Hum. Brain Mapp. 6 6 (1998) 348-357
5. Bajcsy R., and Kovacic S. Multiresolution elastic matching. Comput. Vis. Graph. Image Process. 46 (1989) 1-21
6. Baloch, S., Verma, R., Davatzikos, C., 2007.An anatomical equivalence class based joint transformation-residual descriptor for morphological analysis. In: Intl. Conf. on Information Processing in Medical Imaging.
7. Benjamini Y., and Hochberg Y. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29 (2001) 1165-1188
8. Bishop C.M. Pattern recognition and machine learning (2006), Springer
9. Blezek, D.J., Miller, J.V., 2006. Atlas stratification. In: Intl. Conf. on Medical Image Computing and Computer Assisted Intervention.
10. Bookstein F. Principle warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Patt. Anal. Mach. Intell. 11 6 (1989) 567-585
11. Bookstein F.L. "Voxel-based morphometry" should not be used with imperfectly registered images. NeuroImage 14 (2001) 1454-1462
12. Bro-Nielsen, M., Gramkow, C., 1996. Fast fluid registration of medical images. In: Visualization in Biomedical Computing. pp. 267-276.
13. Buckner R., Head D., Parker J., Fotenos A., Marcus D., Morris J., and Snyder A. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. NeuroImage 23 (2004) 724-738
14. Carneiro, G., Jepson, A., 2003. Multi-scale phase-based local features. In: IEEE Conf. on Computer Vision and Pattern Recognition. Vol. 1. pp. 736-743.
15. Cheung, W., Hamarneh, G., 2007. N-sift: N-dimensional scale invariant feature transform for matching medical images. In: IEEE Intl. Symp. on Biomedical Imaging.
16. Chung M.K., Worsley K.J., Paus T., Cherif C., Collins D.L., Giedd J.N., Rapoport J.L., and Evans A.C. A unified statistical approach to deformation-based morphometry. NeuroImage 14 (2001) 595-606
17. Cortes C., and Vapnik V. Support-vector networks. Mach. Learn. 20 3 (1995) 273-297
18. Coulon O., Mangin J., Poline J., Zilbovicius M., Roumenov D., Samson Y., Frouin V., and Bloch I. Structural group analysis of functional activation maps. NeuroImage 11 (2000) 767-782
19. Davatzikos C. Comments and controversies: why voxel-based morphometric analysis should be used with great caution when characterizing group differences. NeuroImage 23 (2004) 17-20
20. Desikan R.S., Cabral H.J., Hess C.P., Dillon W.P., Glastonbury C.M., Weiner M.W., Schmansky N.J., Greve D.N., Salat D.H., Buckner R.L., Fischl B., and Alzheimer's Disease Neuroimaging Initiative. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease. Brain 132 8 (2009) 2048-2057
21. Dryden I.L., and Mardia K.V. Statistical Shape Analysis (1998), John Wiley & Sons
22. Duchesne S., Caroli A., Geroldi C., Barillot C., Frisoni G.B., and Collins D.L. MRI-based automated computer classification of probable ad versus normal controls. IEEE Trans. Med. Imaging 27 (2008) 509-520
23. Fan Y., Shen D., Gur R.C., Gur R.E., and Davatzikos C. Compare: classification of morphological patterns using adaptive regional elements. IEEE Trans. Med. Imaging 26 1 (2007) 94-105
24. Fergus R., Perona P., and Zisserman A. Weakly supervised scale-invariant learning of models for visual recognition. Int. J. Comput. Vis. 71 3 (2006) 273-303
25. Harris, C., Stephens, M., 1988. A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference. pp. 147-151.
26. Hellier P., Barillot C., Corouge I., Gibaud B., Le Goualher G., Collins D., Evans A., Malandain G., Ayache N., Christensen G., and Johnson H. Retrospective evaluation of intersubject brain registration. IEEE Trans. Med. Imaging 22 9 (September 2003) 1120-1130
27. Kadir T., and Brady M. Saliency, scale and image description. Int. J. Comput. Vis. 45 2 (2001) 83-105
28. Kendall D.G. A survey of the statistical theory of shape. Stat. Sci. 4 2 (1989) 87-120
29. Klein A., and Hirsch J. Mindboggle: a scatterbrained approach to automate brain labeling. NeuroImage 24 (2005) 261-280
30. Klein A., Andersson J., Ardekani B.A., Ashburner J., Avants B., Chiang M.-C., Christensen G.E., Collins D.L., Gee J., Hellier P., Song J.H., Jenkinson M., Lepage C., Rueckert D., Thompson P., Vercauteren T., Woods R.P., Mann J.J., and Parsey R.V. Evaluation of 14 nonlinear deformation algorithms applied to human brain mri registration. NeuroImage 46 3 (2009) 786-802
31. Kloppel S., Stonnington C.M., Chu C., Draganski B., Scahill R.I., Rohrer J.D., Fox N.C., Jack Jr. C.R., Ashburner J., and Frackowiak R.S.J. Automatic classification of MR scans in Alzheimer's disease. Brain 131 (2008) 681-689
32. Koenderink J. The structure of images. Biol. Cybern. 50 (1984) 363-370
33. Lao Z., Shen D., Xue Z., Karacali B., Resnick S.M., and Davatzikos C. Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage 21 (2004) 46-57
34. Lindeberg T. Feature detection with automatic scale selection. Int. J. Comput. Vis. 30 2 (1998) 79-116
35. Lindeberg T., Lidberg P., and Roland P.E. Analysis of brain activation patterns using a 3-D scale-space primal sketch. Hum. Brain Mapp. 7 (1999) 166-194
36. Lowe D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60 2 (2004) 91-110
37. Mangin J., Riviere D., Cachia A., Duchesnay E., Cointepas Y., Papadopoulos-Orfanos D., Collins D.L., Evans A.C., and Regis J. Object-based morphometry of the cerebral cortex. IEEE Trans. Med. Imaging 23 8 (2004) 968-983
38. Marcus D., Wang T., Parker J., Csernansky J., Morris J., and Buckner R. Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented and demented older adults. J. Cogn. Neurosci. 19 (2007) 1498-1507
39. Mikolajczyk K., and Schmid C. Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60 1 (2004) 63-86
40. Nichols T.E., and Holmes A.P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15 (2001) 1-25
41. Ono M., Kubik S., and Abernathy C.D. Atlas of the cerebral sulci (1990), Thieme Medical, New York
42. Pohl, K., Sabuncu, M., 2009. A unified framework for MR based disease classification. In: Intl. Conf. on Information Processing in Medical Imaging.
43. Qiu A., Fennema-Notestine C., Dale A.M., Miller M.I., and Initiative A.D.N. Regional shape abnormalities in mild cognitive impairment and Alzheimer's disease. NeuroImage 45 (2009) 656-661
44. Rohr K. On 3D differential operators for detecting point landmarks. Image Vis. Comput. 15 3 (1997) 219-233
45. Sabuncu, M.R., Balci, S.K., Shenton, M.E., Golland, P., in press. Discovering modes of an image population through mixture modeling. IEEE Trans. Med. Imag. 28 (9), 1473-1487.
46. Studholme C., Drapaca C., Iordanova B., and Cardenas V. Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change. IEEE Trans. Med. Imaging 25 5 (2006) 626-639
47. Sun, Z.Y., Riviere, D., Poupon, F., Regis, J., Mangin, J.-F., 2007. Automatic inference of sulcus patterns using 3d moment invariants. In: Intl. Conf. on Medical Image Computing and Computer Assisted Intervention. pp. 515-522.
48. Sun, Z.Y., Riviere, D., Duchesnay, E., Thirion, B., Poupon, F., Mangin, J.-F., 2008. Defining cortical sulcus patterns using partial clustering based on bootstrap and bagging. In: IEEE Intl. Symp. on Biomedical Imaging. pp. 1629-1632.
49. Talairach J., and Tournoux P. Co-planar stereotactic atlas of the human brain: 3-dimensional proportional system: an approach to cerebral imaging (1988), Georg Thieme Verlag, Stuttgart
50. Thirion B., Pinel P., Tucholka A., Roche A., Ciuciu P., Mangin J.-F., and Poline J.-B. Structural analysis of fMRI data revisited: improving the sensitivity and reliability of fMRI group studies. IEEE Trans. Med. Imaging 26 9 (2007) 1256-1269
51. Thompson P.M., Giedd J.N., Woods R.P., MacDonald D., Evans A.C., and Toga A.W. Growth patterns in the developing human brain detected using continuum-mechanical tensor mapping. Nature 404 6774 (2000) 190-193
52. Toews M., and Arbel T. A statistical parts-based appearance model of anatomical variability. IEEE Trans. Med. Imaging 26 4 (2007) 497-508
53. Toews, M., Collins, D.L., Arbel, T., 2009. Automatically learning cortical folding patterns. In: IEEE Intl. Symp. on Biomedical Imaging. pp. 1330-1333.
54. Toga A.W., Thompson P.M., Mega M.S., Narr K.L., and Blanton R.E. Probabilistic approaches for atlasing normal and disease-specific brain variability. Anat. Embryol. 204 (2001) 267-282
55. Whitwell J.L., Przybelski S.A., Weigand S.D., Knopman D.S., Boeve B.F., Petersen R.C., and Jack Jr. C.R. 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer's disease. Brain 130 (2007) 1777-1786
56. Witkin, A.P., 1983. Scale-space filtering. In: Int. Joint Conf. on Artificial Intelligence. pp. 1019-1021.
57. Young R.A. The Gaussian derivative model for spatial vision. I. retinal mechanisms. Spat. Vis. 2 (1987) 273-293