Classification models for Alzheimer'S Disease detection

Communications in Computer and Information Science

Volumn 384, Issue , 2013, Pages 193-202

  Anagnostopoulos, Christos Nikolaos ^a   Giannoukos, Ioannis ^a   Spenger, Christian ^b   Simmons, Andrew ^c   Mecocci, Patrizia ^d   Soininen, Hikka ^e   Kłoszewska, Iwona ^f   Vellas, Bruno ^g   Lovestone, Simon ^c   Tsolaki, Magda ^h  

^a UNIVERSITY OF THE AEGEAN   (Greece)

^b Intervention and Technology ^*  (Sweden)

^c KING'S COLLEGE LONDON   (United Kingdom)

^d UNIVERSITY OF PERUGIA   (Italy)

^e KUOPIO UNIVERSITY HOSPITAL   (Finland)

^f MEDICAL UNIVERSITY OF LODZ   (Poland)

^g Hopitaux de Toulouse   (France)

^h ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Alheimer's Disease detection; Classification fusion; Mild Cognitive impairment

Indexed keywords

IMAGE PROCESSING; NEURODEGENERATIVE DISEASES;

CLASSIFICATION FUSION; CLASSIFICATION MODELS; COMBINED CLASSIFIERS; DISEASE DETECTION; MILD COGNITIVE IMPAIRMENTS; MILD COGNITIVE IMPAIRMENTS (MCI); PATTERN CLASSIFICATION PROBLEMS; SUBJECT CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 84885167624     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-41016-1_21     Document Type: Article

References (26)

1. 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 34, 939-944 (1984)
2. Dubois, B., Feldman, H.H., Jacova, C., DeKosky, S.T., Barberger-Gateau, P., Cummings, J., Delacourte, A., Galasko, D., Gauthier, S., Jicha, G., Meguro, K., O'Brien, J., Pasquier, F., Robert, P., Rossor, M., Salloway, S., Stern, Y., Visser, P.J., Scheltens, P.: Research criteria for the diagnosis of alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet. Neurol. 6, 734-746 (2007)
3. Lehmann, A., Koenig, T., Jelic, V., Prichep, L., John, R.E., Wahlund, L.O., Dodge, Y., Dierks, T.: Application and comparison of classification algorithms for recognition of alzheimer's disease in electrical brain activity (EEG). J. Neurosci. Meth. 161, 342-350 (2007)
4. Chaves, R., Ramírez, J., Górriz, J.M., López, M., Salas-Gonzalez, D., Alvarez, I., Segovia, F.: SVM-based computer-aided diagnosis of the alzheimer's disease using t-test NMSE feature selection with feature correlation weighting. Neurosci. Lett. 461, 293-297 (2009)
5. López, M.M., Ramírez, J., Górriz, J.M., Alvarez, I., Salas-Gonzalez, D., Segovia, F., Chaves, R.: SVM-based CAD system for early detection of the alzheimer's disease using kernel PCA and LDA. Neurosci. Lett. 464, 233-238 (2009)
6. 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 39, 1186-1197 (2008)
7. Colliot, O., Chételat, G., Chupin, M., Desgranges, B., Magnin, B., Benali, H., Dubois, B., Garnero, L., Eustache, F., Lehéricy, S.: Discrimination between alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248, 194-201 (2008)
8. Tripoliti, E.E., Fotiadis, D.I., Argyropoulou, M.: An automated supervised method for the diagnosis of alzheimer's disease based on fMRI data using weighted voting schemes. In: Proc. of IEEE International Workshop on Imaging Systems and Techniques, pp. 340-345. IEEE, Chania (2008)
9. Liu, Y., Paajanen, T., Zhang, Y., Westman, E., Wahlund, L.O., Simmons, A., Tunnard, C., Sobow, T., Mecocci, P., Tsolaki, M., Vellas, B., Muehlboeck, S., Evans, A., Spenger, C., Lovestone, S., Soininen, H.: Combination analysis of neuropsychological tests and structural MRI measures in differentiating AD, MCI and control groups-The AddNeuroMed study. Neurobiol. Aging 32, 1198-1206 (2011)
10. Lovestone, S., Francis, P., Strandgaard, K.: Biomarkers for disease modification trials-the innovative medicines initiative and AddNeuroMed. J. Nutr. Health Aging 11, 359-361 (2007)
11. Jack Jr, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Whitwell, J.L., Ward, C., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hil, D.L., 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. Im. 27, 685-691 (2008)
12. Simmons, A., Westman, E., Muehlboeck, S., Mecocci, P., Vellas, B., Tsolaki, M., Kloszewska, I., Wahlund, L.O., Soininen, H., Lovestone, S., Evans, A., Spenger, C.: MRI measures of alzheimer's disease and the AddNeuroMed study. Ann. NY. Acad. Sci. 1180, 47-55 (2009)
13. Simmons, A., Westman, E., Muehlboeck, S., Mecocci, P., Vellas, B., Tsolaki, M., Kloszewska, I., Wahlund, L.O., Soininen, H., Lovestone, S., Evans, A., Spenger, C.: The AddNeuroMed framework for multi-centre MRI assessment of longitudinal changes in alzheimer's disease: experience from the first 24 months. Int. J. Ger. Psych. 26, 75-82 (2011)
14. Folstein, M.F., Folstein, S.E., McHugh, P.R.: Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J. Psychiat. Res. 12, 189-198 (1975)
15. Hughes, C.P., Berg, L., Danziger, W.L., Coben, L.A., Martin, R.L.: A new clinical scale for the staging of dementia. Brit. J. Psychiat. 140, 566-572 (1982)
16. Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., Kokmen, E.: Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol-Cigago 56(6), 303-308 (1999)
17. Fischl, B., Dale, A.M.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. of the National Academy of Sciences 97, 11050-11055 (2000)
18. Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341-355 (2002)
19. Fischl, B., Salat, D.H., van der Kouwe, A.J., Makris, N., Segonne, F., Quinn, B.T., Dale, A.M.: Sequence-independent segmentation of magnetic resonance images. Neuroimage 23, S69-S84 (2004)
20. Segonne, F., Dale, A.M., Busa, E., Glessner, M., Salat, D., Hahn, H.K., Fischl, B.: A hybrid approach to the skull stripping problem in MRI. Neuroimage 22, 1060-1075 (2004)
21. Fischl, B., Liu, A., Dale, A.M.: Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE T. Med. Imaging 20, 70-80 (2001)
22. Fischl, B., Sereno, M.I., Dale, A.M.: Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. II: Inflation, flattening, and a surfacebased coordinate system. Neuroimage 9, 195-207 (1999)
23. Fischl, B., Sereno, M.I., Tootell, R.B., Dale, A.M.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272-284 (1999)
24. Hermes medical solutions (2012), http://www.hermesmedical.com (accessed March 12, 2013)
25. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE T. Pattern Anal. 20, 226-239 (1998)
26. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 38, 337-374 (2000)