Independent component analysis-based classification of alzheimer’s disease from segmented MRI data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9107, Issue , 2015, Pages 78-87

  Khedher, L ^a   Ramírez, Javier ^a   Manuel Gorriz J ^a   Brahim, A ^a   Illan I A ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Alzheimer s disease; Computer aided diagnosis; Independent component analysis; Magnetic resonance imaging; Mild cognitive impairment; Supervised learning; Support vector machine

Indexed keywords

BRAIN MAPPING; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED ANALYSIS; COMPUTER AIDED DIAGNOSIS; COMPUTER AIDED INSTRUCTION; DIAGNOSIS; FEATURE EXTRACTION; IMAGE PROCESSING; MACHINE COMPONENTS; MAGNETIC RESONANCE IMAGING; NEURODEGENERATIVE DISEASES; NEUROIMAGING; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES; VECTOR SPACES;

ALZHEIMER; AUTOMATIC CLASSIFICATION; INDEPENDENT COMPONENT ANALYSIS(ICA); INDEPENDENT COMPONENTS; MILD COGNITIVE IMPAIRMENTS; MILD COGNITIVE IMPAIRMENTS (MCI); STATISTICAL PARAMETRIC MAPPING; SUPERVISED LEARNING METHODS;

INDEPENDENT COMPONENT ANALYSIS;

EID: 84937501582     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-18914-7_9     Document Type: Conference Paper

References (18)

1. Alzheimer’s Association, Alzheimer’s News (2013), http://www.alz.org/news and events facts and figures report.asp
2. Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehricy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O.: Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage 56, 766–781 (2011)
3. Illán, I.A., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D., López, M.M., Segovia, F., Chaves, R., Gómez-Rio, M., Puntonet, C.G.: 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Information Sciences 181, 903–916 (2011)
4. Illán, I.A., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D., López, M.M., Segovia, F., Padilla, P., Puntonet, C.G.: Projecting independent components of SPECT images for computer aided diagnosis of Alzheimers disease. Pattern Recognition Letters 31, 1342–1347 (2010)
5. Duin, R.P.W.: Classifiers in almost empty spaces. In: Proceedings of the 15th International Conference on Pattern Recognition, vol. 2, pp. 1–7 (2000)
6. Magnin, B., Mesrob, L., Kinkingnehun, S., Pelegrini-Issac, M., Calliot, O., Sarazin, M., Dubais, B., Lehericy, S., Benali, H.: Support vector machine-based classification of alzheimer’s disease from whole-brain anatomical mri. Neuroradiology 51, 73–83 (2009)
7. Jaramillo, D., Rojas, I., Valenzuela, O., Garcia, I., Prieto, A.: Advanced systems in medical decision-making using intelligent computing. Application to magnetic resonance imaging. In: International Joint Conference on Neural Networks (IJCNN) (2012)
8. Padilla, P., Lopez, M., Gorriz, J.M., Ramirez, J., Salas-Gonzalez, D., Alvarez, I.: NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease. IEEE Trans. Med. Imaging 31, 207–216 (2012)
9. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Haussler, D. (ed.) Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pp. 144–152. ACM Press, Pittsburgh (1992)
10. Martinez-Murcia, F.J., Grriz, J.M., Ramrez, J., Moreno-Caballero, M., Gomez- Rio, M.: Parkinson’s Progression Markers Initiative. Parametrization of textural patterns in 123i-ioflupane imaging for the automatic detection of parkinsonism. Medical Physics 41, 012502 (2013)
11. Khedher, L., Ramrez, J., Grriz, J.M., Brahim, A., Segovia, F.: Early diagnosis of Alzheimer’s disease based on Partial Least Squares, Principal Component Analysis and Support Vector Machine using segmented MRI images. Neurocomputing 151, 139–150 (2015)
12. Chaves, R., Ramrez, J., Grriz, J.M., Lpez, 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)
13. Ashburner, J., Friston, K.: Human Brain Function (2003)
14. Psychiatry SBMGD, Vbm toolboxes. University of Jena (2013), http://dbm.neuro.uni-jena.de/vbm8/VBM8-Manual.pdf
15. Ashburner, J., Barnes, G., Chen, C., Daunizeau, J., Flandin, G., Friston, K.: SPM8 manual. In: Functional Imaging Laboratory. Institute of Neurology, London (2012)
16. Stoeckel, J., Ayache, N., Malandain, G., Malick Koulibaly, P., Ebmeier, K.P., Darcourt, J.: Automatic classification of spect images of Alzheimer’s disease patients and control subjects. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 654–662. Springer, Heidelberg (2004)
17. Stoeckel, J., Malandain, G., Migneco, O., Malick Koulibaly, P., Robert, P., Ayache, N., Darcourt, J.: Classification of SPECT images of normal subjects versus images of alzheimer’s disease patients. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 666–674. Springer, Heidelberg (2001)
18. Khedher, L., Ramrez, J., Grriz, J.M., Brahim, A.: Automatic classification of segmented MRI data combining Independent Component Analysis and Support Vector Machines. In: Innovation in Medicine and Healthcare, InMed, vol. 207. Lecture notes in IOS Press (2014)