Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier

NeuroImage

Volumn 58, Issue 3, 2011, Pages 785-792

  Abdulkadir, Ahmed ^a,b   Mortamet, Bénédicte ^b   Vemuri, Prashanthi ^c   Jack Jr , Clifford R ^c   Krueger, Gunnar ^b   Klöppel, Stefan ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

^b Siemens Suisse SA   (Switzerland)

^c MAYO CLINIC   (United States)

Author keywords

Alzheimer's disease; Magnetic resonance imaging; MRI; Multi site study; Support vector machines (SVM)

Indexed keywords

AGED; ALZHEIMER DISEASE; ARTICLE; COMPUTER SYSTEM; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; MAJOR CLINICAL STUDY; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; VALIDATION STUDY;

ALZHEIMER DISEASE; ARTIFICIAL INTELLIGENCE; COMPUTERS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; SUPPORT VECTOR MACHINES;

EID: 80052169077     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2011.06.029     Document Type: Article

References (30)

1. Acosta-Cabronero J., Williams G.B., Pereira J.M.S., Pengas G., Nestor P.J. The impact of skull-stripping and radio-frequency bias correction on grey-matter segmentation for voxel-based morphometry. Neuroimage 2008, 39:1654-1665.
2. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage 2007, 38:95-113.
3. Ashburner J., Friston K.J. Voxel-based morphometry - the methods. Neuroimage 2000, 11:805-821.
4. Ashburner J., Friston K.J. Unified segmentation. Neuroimage 2005, 26:839-851.
5. Baron J.C., Chételat G., Desgranges B., Perchey G., Landeau B., de la Sayette V., Eustache F. In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. Neuroimage 2001, 14:298-309.
6. Boser B.E., Guyon I.M., Vapnik V.N. A training algorithm for optimal margin classifiers. Fifth Annual Workshop on Computational Learning Theory, Pittsburgh. ACM 1992, 144-152.
7. Braak H., Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991, 82:239-259.
8. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines (Software available at). http://www.csie.ntu.edu.tw/~cjlin/libsvm.
9. Cortes C., Vapnik V. Support-vector networks. Mach. Learn 1995, 20:273-297.
10. Cuingnet R., Gérardin E., Tessieras J., Auzias G., Lehéricy S., Habert M.-O., Chupin M., Benali H., Colliot O., Alzheimer's Disease Neuroimaging Initiative Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 2011, 56:766-781.
11. Fox N.C., Freeborough P.A., Rossor M.N. Visualisation and quantification of rates of atrophy in Alzheimer's disease. Lancet 1996, 348:94-97.
12. Franke K., Ziegler G., Klöppel S., Gaser C., the Alzheimer's Disease Neuroimaging Initiative Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage 2010, 50:883-892.
13. Gunter J.L., Bernstein M.A., Borowski B.J., Ward C.P., Britson P.J., Felmlee J.P., Schuff N., Weiner M., Jack C.R. Measurement of MRI scanner performance with the ADNI phantom. Med. Phys. 2009, 36:2193-2205.
14. Huppertz H.-J., Kröll-Seger J., Klöppel S., Ganz R.E., Kassubek J. Intra- and interscanner variability of automated voxel-based volumetry based on a 3D probabilistic atlas of human cerebral structures. Neuroimage 2010, 49:2216-2224.
15. Jack C.R., Petersen R.C., O'Brien P.C., Tangalos E.G. MR-based hippocampal volumetry in the diagnosis of Alzheimer's disease. Neurology 1992, 42:183-188.
16. Jack C.R., Bernstein M.A., Fox N.C., Thompson P., Alexander G., Harvey D., Borowski B., Britson P.J.L., Whitwell J., Ward C., Dale A.M., Felmlee J.P., Gunter J.L., Hill D.L.G., Killiany R., Schuff N., Fox-Bosetti S., Lin C., Studholme C., DeCarli C.S., Krueger G., Ward H.A., Metzger G.J., Scott K.T., Mallozzi R., Blezek D., Levy J., Debbins J.P., Fleisher A.S., Albert M., Green R., Bartzokis G., Glover G., Mugler J., Weiner M.W. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 2008, 27:685-691.
17. Klauschen F., Goldman A., Barra V., Meyer-Lindenberg A., Lundervold A. Evaluation of automated brain MR image segmentation and volumetry methods. Hum. Brain Mapp. 2009, 30:1310-1327.
18. Klöppel S., Stonnington C.M., Chu C., Draganski B., Scahill R.I., Rohrer J.D., Fox N.C., Jack C.R., Ashburner J., Frackowiak R.S.J. Automatic classification of MR scans in Alzheimer's disease. Brain 2008, 131:681-689.
19. Klöppel S., Stonnington C.M., Chu C., Draganski B., Scahill R.I., Rohrer J.D., Fox N.C., Ashburner J., Frackowiak R.S.J. A plea for confidence intervals and consideration of generalizability in diagnostic studies. Brain 2009, 132.
20. Magnin B., Mesrob L., Kinkingnéhun S., Pélégrini-Issac M., Colliot O., Sarazin M., Dubois B., Lehéricy S., Benali H. Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI. Neuroradiology 2009, 51:73-83.
21. McKhann G., Drachman D., Folstein M., Katzman R., Price D., Stadlan E.M. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's disease. Neurology 1984, 34:939-944.
22. Moorhead T.W.J., Gountouna V.-E., Job D.E., McIntosh A.M., Romaniuk L., Lymer G.K.S., Whalley H.C., Waiter G.D., Brennan D., Ahearn T.S., Cavanagh J., Condon B., Steele J.D., Wardlaw J.M., Lawrie S.M. Prospective multi-centre Voxel Based Morphometry study employing scanner specific segmentations: procedure development using CaliBrain structural MRI data. BMC Med. Imaging 2009, 9:8.
23. Morris J.C., Price A.L. Pathologic correlates of nondemented aging, mild cognitive impairment, and early-stage Alzheimer's disease. J. Mol. Neurosci. 2001, 17:101-118.
24. Mueller S.G., Weiner M.W., Thal L.J., Petersen R.C., Jack C., Jagust W., Trojanowski J.Q., Toga A.W., Beckett L. The Alzheimer's disease neuroimaging initiative. Neuroimaging Clin. N. Am. 2005, 15:869-877. (xi-xii).
25. Plant C., Teipel S.J., Oswald A., Böhm C., Meindl T., Mourao-Miranda J., Bokde A.W., Hampel H., Ewers M. Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease. Neuroimage 2010, 50:162-174.
26. Shuter B., Yeh I.B., Graham S., Au C., Wang S.-C. Reproducibility of brain tissue volumes in longitudinal studies: effects of changes in signal-to-noise ratio and scanner software. Neuroimage 2008, 41:371-379.
27. Stonnington C.M., Tan G., Klöppel S., Chu C., Draganski B., Jack C.R., Chen K., Ashburner J., Frackowiak R.S.J. Interpreting scan data acquired from multiple scanners: a study with Alzheimer's disease. Neuroimage 2008, 39:1180-1185.
28. Vapnik V.N. Statistical Learning Theory 1998, Wiley Interscience, New York.
29. Vemuri P., Gunter J.L., Senjem M.L., Whitwell J.L., Kantarci K., Knopman D.S., Boeve B.F., Petersen R.C., Jack C.R. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 2008, 39:1186-1197.
30. Whitwell J.L., Przybelski S.A., Weigand S.D., Knopman D.S., Boeve B.F., Petersen R.C., Jack C.R. 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer's disease. Brain 2007, 130:1777-1786.