Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter August 12, 2016

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

  • Golrokh Mirzaei , Anahita Adeli and Hojjat Adeli EMAIL logo

Abstract

Alzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

References

Adeli, H., ed. (1994). Advances in Design Optimization (London: Chapman and Hall).10.1201/9781482267549Search in Google Scholar

Adeli, H. and Ghosh-Dastidar, S. (2010). Automated EEG-based Diagnosis of Neurological Disorders – Inventing the Future of Neurology (Boca Raton, FL: CRC Press, Taylor & Francis).10.1201/9781439815328Search in Google Scholar

Adeli, H. and Hung, S.L. (1995). Machine Learning Neural Networks, Genetic Algorithms, and Fuzzy Systems (New York: John Wiley and Sons).Search in Google Scholar

Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2005a). Alzheimer’s disease: models of computation and analysis of EEGs. Clin. EEG Neurosci. 36, 131–140.10.1201/9781439815328-c11Search in Google Scholar

Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2005b). Alzheimer’s disease and models of computation: imaging, classification, and neural models. J. Alzheimers Dis. 7, 255–262.10.1201/9781439815328-c10Search in Google Scholar

Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2008). A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer’s disease. Neurosci. Lett. 444, 190–194.10.1201/9781439815328-c12Search in Google Scholar

Ahmadlou, M. and Adeli, H. (2010). Enhanced probabilistic neural network with local decision circles: a robust classifier. Integr. Compu.-Aid. E. 17, 197–210.10.3233/ICA-2010-0345Search in Google Scholar

Ahmadlou, M., Adeli, H., and Adeli, A. (2010). New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. J. Neural Transm. 117, 1099–1109.10.1007/s00702-010-0450-3Search in Google Scholar PubMed

Ahmadlou, A., Adeli, H., and Adeli, A. (2011). Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer’s disease. Alzheimer Dis. Assoc. Disord. 25, 85–92.10.1097/WAD.0b013e3181ed1160Search in Google Scholar PubMed

Amezquita-Sanchez, J.P. and Adeli, H. (2015). A new MUSIC-empirical wavelet transform methodology for time-frequency analysis of noisy nonlinear and non-stationary signals. Digit. Signal Process. 45, 55–68.10.1016/j.dsp.2015.06.013Search in Google Scholar

Amezquita-Sanchez, J.P., Adeli, A., and Adeli, H. (2016). A new methodology for automated diagnosis of mild cognitive impairment (MCI) using magnetoencephalography (MEG). Behav. Brain Res. 305, 174–180.10.1016/j.bbr.2016.02.035Search in Google Scholar PubMed

Anand, S.M., Rao, M.M., Prabhu, N.S., Simeon, S.D., Karthikeya, D., and Rashmi, S. (2009). Automated diagnosis of early Alzheimer’s disease using fuzzy neural network. Springer, 4th European Conference of the International Federation for Medical and Biological Engineering, Volume 22 of the series IFMBE Proceedings, pp. 1455–1458.Search in Google Scholar

Ashburner, J. and Friston, K.J. (2000). Voxel-based morphometry: the methods. Neuroimage 11, 805–821.10.1006/nimg.2000.0582Search in Google Scholar

Ayutyanont, N., Chen, K., Villemagne, V., O’Keefe, G., Liu, X., Reschke, C., Lee, W., Venditti, J., Bandy, D., Yu, M., et al. (2009). Using the artificial neural network to discriminate between normal controls with different APOE ε4 genotypes and probable AD cases in PIB-PET studies. IEEE ICME International Conference on Complex Medical Engineering (Tempe, AZ, USA), pp. 1–5.Search in Google Scholar

Bauer, S., Nolte, N.P., and Reyes, M. (2011). Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. Med. Image Comput. Comput. Assist. Interv. 14, 354–361.10.1007/978-3-642-23626-6_44Search in Google Scholar

Bazin, P.L. and Pham, D.L. (2008). Homeomorphic brain image segmentation with topological and statistical atlases. Med. Image Anal. 12, 616–625.10.1016/j.media.2008.06.008Search in Google Scholar

Besthorn, C., Zerfass, R., Geiger-Kabisch, C., Sattel, H., Daniel, S., Schreiter-Gasser, U., and Förstl, H. (1997). Discrimination of Alzheimer’s disease and normal aging by EEG data. Electroencephalogr. Clin. Neurophysiol. 103, 241–248.10.1016/S0013-4694(97)96562-7Search in Google Scholar

Boccardi, M., Ganzola, R., Bocchetta, M., Pievani, M., Redolfi, A., Bartzokis, G., Camicioli, R., Csernansky, J.G., de Leon, M.J., deToledo-Morrell, L., et al. (2011). Survey of protocols for the manual segmentation of the hippocampus: preparatory steps towards a joint EADC-ADNI harmonized protocol. J. Alzheimers Dis. 26(Suppl 3), 61–75.10.3233/JAD-2011-0004Search in Google Scholar

Boer, R., Vrooman, H.A., Van der Lijn, F., Vernooij, M.W., Ikram, M.A., van der Lugt, A., Breteler, M.M., and Niessen, W.J. (2009). White matter lesion extension to automatic brain tissue segmentation on MRI. Neuroimage 45, 1151–1161.10.1016/j.neuroimage.2009.01.011Search in Google Scholar

Braak, H. and Braak, E. (1998). Evolution of neuronal changes in the course of Alzheimer’s disease. J. Neural Transm. Suppl. 53, 127–140.10.1007/978-3-7091-6467-9_11Search in Google Scholar

Bresser, J.D., Portegies, M.P., Leemans, A., Biessels, G.J., Kappelle, L.J., and Viergever, M.A. (2011). A comparison of MR based segmentation methods for measuring brain atrophy progression. Neuroimage 54, 760–768.10.1016/j.neuroimage.2010.09.060Search in Google Scholar

Bricq, S., Collet, C., and Armspach, J.P. (2008). Unifying framework for multimodal brain MRI segmentation based on hidden Markov chains. Med. Image Anal. 12, 639–652.10.1016/j.media.2008.03.001Search in Google Scholar

Buscema, M., Grossi, E., Snowdon, D., Antuono, P., Intraligi, M., Maurelli, G., and Savarè, R. (2004). Artificial neural networks and artificial organisms can predict Alzheimer pathology in individual patients only on the basis of cognitive and functional status. NeuroInformatics 2, 399–416.10.1385/NI:2:4:399Search in Google Scholar

Cabezas, M., Oliver, A., Liado, X., Freixenet, J., and Cuadra, M.B. (2011). A review of atlas-based segmentation for magnetic resonance brain images. Comput. Methods Programs Biomed. 104, e158–e177.10.1016/j.cmpb.2011.07.015Search in Google Scholar PubMed

Caldairou, B., Passat, N., Habas, P.A., Studholme, C., and Rousseau, F. (2011). A non-local fuzzy segmentation method: application to brain MRI. Pattern Recognit. 44, 1916–1927.10.1007/978-3-642-03767-2_74Search in Google Scholar

Casanova, R., Hsu, F.C., Espeland, M.A.; Alzheimer’s Disease Neuroimaging Initiative. (2012). Classification of structural MRI images in Alzheimer’s disease from the perspective of ill-posed problems. PLoS One 7, e44877.10.1371/journal.pone.0044877Search in Google Scholar PubMed PubMed Central

Castillo, E., Peteiro-Barral, D., Guijarro Berdinas, B., and Fontenla-Romero, O. (2015). Distributed one-class support vector machine. Int. J. Neural Syst. 25, 1550029.10.1142/S012906571550029XSearch in Google Scholar PubMed

Chai, C. and Wong, Y.D. (2015). Fuzzy cellular automata models for signalized intersections. Comput.-Aided Civ. Infrastruct. Eng. 30, 951–964.10.1111/mice.12181Search in Google Scholar

Chapelle, O., Sindhwani, V., and Keerthi, S. (2008). Optimization techniques for semi-supervised support vector machines. J. Mach. Learn. Res. 9, 203–233.Search in Google Scholar

Chen, W., Li, S., Jia, F., and Zhang, X. (2011). Segmentation of hippocampus based on ROI atlas registration. 2011 IEEE International Symposium on IT in Medicine and Education (Cuangzhou, China), pp. 226–230.10.1109/ITiME.2011.6130821Search in Google Scholar

Chen, Y., Juttukonda, M., Lee, Y.Z., Su, Y., Espinoza, F., Lin, W., Shen, D., Lulash, D., and An, H. (2014). MRI based attenuation correction for PET/MRI via MRF segmentation and sparse regression estimated CT. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) (Beijing, China), pp. 1364–1367.10.1109/ISBI.2014.6868131Search in Google Scholar

Cherry, S. (2009). Multimodality imaging: beyond PET/CT and SPECT/CT. Semin. Nucl. Med. 39, 348–353.10.1053/j.semnuclmed.2009.03.001Search in Google Scholar PubMed PubMed Central

Cho, J.H., Cho, J., Hwang, S., Ahn, S., Ryu, E.K., and Lee, C. (2011). A new technological fusion of PET and MRI for brain imaging. J. Anal. Sci. Technol. 2, 30–35.10.5355/JAST.2011.30Search in Google Scholar

Chou, J.S. and Pham, A.D. (2015). Smart artificial firefly colony-based support vector regression for enhanced forecasting in civil engineering. Comput.-Aided Civ. Infrastruct. Eng. 30, 715–732.10.1111/mice.12121Search in Google Scholar

Chupin, M., Chetelat, G., Lemieux, L., Dubois, B., Garnero, L., and Benali, H. (2008). Fully automatic hippocampus segmentation discriminates between early Alzheimer’s disease and normal aging. 5th IEEE International Symposium on Biomedical Imaging, From Nano to Macro (Paris, France), pp. 97–100.10.1109/ISBI.2008.4540941Search in Google Scholar

Chyzhyk, D., Graña, M., Ongur, D., and Shinn, A.K. (2015). Discrimination of schizophrenia auditory hallucinators from never hallucinators through machine learning of resting-state functional MRI. Int. J. Neural Syst. 25, 1550007.10.1142/S0129065715500070Search in Google Scholar PubMed PubMed Central

Claus, J.J., Ongerboer de Visser, B.W., Bour, L.J., Walstra, G.J., Hijdra, A., Verbeeten, B., Jr, Van Royen, E.A., Kwa, V.I., and van Gool, W.A. (2000). Determinants of quantitative spectral electroencephalography in early Alzheimer’s disease: cognitive function, regional cerebral blood flow, and computed tomography. Dement. Geriatr. Cogn. Disord. 11, 81–89.10.1159/000017219Search in Google Scholar PubMed

Costantini, G., Casali, D., and Todisco, M. (2010). An SVM based classification method for EEG signals. Proceedings of the 14th WSEAS International Conference on Circuits (ICC ’10) (Wisconsin, USA), pp. 107–109.Search in Google Scholar

Davidson, P.R., Jones, R.D., and Peiris, M.T. (2007). EEG-based lapse detection with high temporal resolution. IEEE Trans. Biomed. Eng. 54, 832–839.10.1109/TBME.2007.893452Search in Google Scholar PubMed

Daya, R.P., Bhandari, J.K., Hui, P.A., Tian, Y., Farncombe, T., and Mishra, R.K. (2014). Effects of MK-801 treatment across several pre-clinical analyses including a novel assessment of brain metabolic function utilizing PET and CT fused imaging in live rats. Neuropharmacology 77, 325–333.10.1016/j.neuropharm.2013.10.001Search in Google Scholar PubMed

deFigueiredo, R.J., Shankle, W.R., Maccato, A., Dick, M.B., Mundkur, P., Mena, I., and Cotman, C.W. (1995). Neural-network based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain. Proc. Natl. Acad. Sci. USA. 92, 5530–5534.10.1073/pnas.92.12.5530Search in Google Scholar PubMed PubMed Central

Deleforge, A., Forbes, F., and Horaud, R. (2015). Acoustic space learning for sound-source separation and localization on binatural manifolds. Int. J. Neural Syst. 25, 1440003.10.1142/S0129065714400036Search in Google Scholar PubMed

Du, A.T., Schuff, N., Kramer, J.H., Ganzer, S., Zhu, X.P., Jagust, W.J., Miller, B.L., Reed, B.R., Mungas, D., Yaffe, K., et al. (2004). Higher atrophy rate of entorhinal cortex than hippocampus in Alzheimer’s disease. Neurology 62, 422–427.10.1212/01.WNL.0000106462.72282.90Search in Google Scholar

Duchesne, S., Caroli, A., Geroldi, C., Barillot, C., Frisoni, G.B., and Collins, D.L. (2008). MRI-based automated computer classification of probable AD versus normal controls. IEEE Trans. Med. Imaging 27, 509–520.10.1109/TMI.2007.908685Search in Google Scholar PubMed

Escudero, J., Zajicek, J.P., and Ifeachor, E. (2011). Early detection and characterization of Alzheimer’s disease in clinical scenarios using bioprofile concepts and K-means. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 6470–6473.10.1109/IEMBS.2011.6091597Search in Google Scholar PubMed

Ferdowsi, S., Sanei, S., and Abolghasemi, V. (2015). A predictive modeling to analyze data in EEG-fMRI experiments. Int. J. Neural Syst. 25, 1440008.10.1142/S0129065714400085Search in Google Scholar PubMed

Fiorina, E., Bellotti, R., Cerello, P., and Chincarini, A. (2012). Fully automated hippocampus segmentation with virtual ant colonies. 2012 25th International Symposium on Computer-Based Medical Systems (Rome, Italy), pp. 1–6.10.1109/CBMS.2012.6266303Search in Google Scholar

Forero Mendoza, L., Vellasco, M., and Figueiredo, K. (2014). Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models. Int. J. Neural Syst. 24, 1450031.10.1142/S0129065714500312Search in Google Scholar PubMed

Friedrich, J., Urbancziky, R., and Senn, W. (2014). Code-specific learning rules improve action selection by populations of spiking neurons. Int. J. Neural Syst. 24, 1450002.10.1142/S0129065714500026Search in Google Scholar PubMed

Gado, M., Hughes, C.P., Danziger, W., and Chi, D. (1983). Aging, dementia, and brain atrophy: a longitudinal computed tomographic study. AJNR Am. J. Neuroradiol. 4, 699–702.Search in Google Scholar

Geuze, E., Vermetten, E., and Bremner, J.D. (2005). MR-based in vivo hippocampal volumetrics: 1. Review of methodologies currently employed. Mol. Psychiatry 10, 147–159.10.1038/sj.mp.4001580Search in Google Scholar PubMed

Ghosh-Dastidar, S., Adeli, H., and Dadmehr, N. (2008). Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans. Biomed. Eng. 55, 512–518.10.1109/TBME.2007.905490Search in Google Scholar PubMed

Ghuffar, S., Brosch, N., Pfeifer, N., and Gelautz, M. (2014). Motion estimation and segmentation in depth and intensity video. Integr. Compu.-Aid. E. 21, 203–218.10.3233/ICA-130456Search in Google Scholar

Goebel, R., Esposito, F., and Formisano, E. (2006). Analysis of FIAC data with BrainVoyager QX: from single-subject to cortically aligned group GLM analysis and self-organizing group ICA. Hum. Brain Mapp. 27, 392–401.10.1002/hbm.20249Search in Google Scholar PubMed PubMed Central

Gonçalves, N., Nikkilä, J., and Vigário, R. (2014). Self-supervised MRI tissue segmentation by discriminative clustering. Int. J. Neural Syst. 24, 1450004.10.1142/S012906571450004XSearch in Google Scholar PubMed

Gurubel, K.J., Alanis, A.Y., Sanchez, E.N., and Carlos-Hernandez, S. (2014). A neural observer with time-varying learning rate: analysis and applications. Int. J. Neural Syst. 24, 1450011.10.1142/S0129065714500117Search in Google Scholar PubMed

Haijema, R. and Hendrix, E.M.T. (2014). Traffic responsive control of intersections with predicted arrival times: a Markovian approach. Comput.-Aided Civ. Infrastruct. Eng. 29, 123–139.10.1111/mice.12018Search in Google Scholar

Henderson, G., Ifeacjor, E., Hudson, N., Goh, C., Outram, N., Wimalaratna, S., Del Percio, C., and Vecchio, F. (2006). Development and assessment of methods for detecting dementia using the human electencephalogram. IEEE Trans. Biomed. Eng. 53, 1557–1568.10.1109/TBME.2006.878067Search in Google Scholar

Henneman, W.J., Sluimer, J.D., Barnes, J., van der Flier, W.M., Sluimer, I.C., Fox, N.C., Scheltens, P., Vrenken, H., and Barkhof, F. (2009). Hippocampal atrophy rates in Alzheimer disease: added value over whole brain volume measures. Neurology 72, 999–1007.10.1212/01.wnl.0000344568.09360.31Search in Google Scholar

Herrera, L.J., Rojas, I., Pomares, H., Guillen, A., and Banos, O. (2013). Classification of MRI images for Alzheimer’s disease detection. 2013 IEEE International Conference on Social Computing (SocialCom) (Alexandria, VA, USA), pp. 846–851.10.1109/SocialCom.2013.127Search in Google Scholar

Hill, D.L.G., Batchelor, P.G., Holden, M., and Hawkes, D.J. (2001). Medical image registration. Phys. Med. Biol. 46, R1–R45.10.1088/0031-9155/46/3/201Search in Google Scholar

Hsu, W.Y. (2015). Assembling a multi-feature EEG classifier for left-right motor data using wavelet-based fuzzy approximate entropy for improved accuracy. Int. J. Neural Syst. 25, 1550037.10.1142/S0129065715500379Search in Google Scholar

Huang, Y., Yang, B., Zaza, S., and Liu, W. (2013). A fuzzy approach to assess the indication of dementia based on magnetic reasoning imaging. Proceedings of 2013 International Conference on Fuzzy Theory and Its Application (Taipei, Taiwan), pp. 328–333.10.1109/iFuzzy.2013.6825459Search in Google Scholar

Huang, Y., Beck, J.L., Wu, S., and Li, H. (2014). Robust Bayesian compressive sensing for signals in structural health monitoring. Comput.-Aided Civ. Infrastruct. Eng. 29, 160–179.10.1111/mice.12051Search in Google Scholar

Hung, S.L. and Adeli, H. (1993). Parallel backpropagation learning algorithms on Cray Y-MP8/864 supercomputer. Neurocomputing 5, 287–302.10.1016/0925-2312(93)90042-2Search in Google Scholar

Huo, J., Gao, Y., Yang, W., and Yin, H. (2014). Multi-instance dictionary learning for detecting abnormal event detection in surveillance videos. Int. J. Neural Syst. 24, 1430010.10.1142/S0129065714300101Search in Google Scholar PubMed

Jack, C.R. and Holtzman, D.M. (2013). Biomarker modeling of Alzheimer’s disease. Neuron 80, 1347–1358.10.1016/j.neuron.2013.12.003Search in Google Scholar

Jack, C.R., Petersen, R.C., Xu, Y., O’Brien, P.C., Smith, G.E., Ivnik, R.J., Boeve, B.F., Tangalos, E.G. and Kokmen, E. (2000). Rates of hippocampal atrophy correlate with change in clinical status in aging and Alzheimer’s disease. Neurology 55, 484–489.10.1212/WNL.55.4.484Search in Google Scholar

Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Whitwell, J., Ward, C., et al. (2008). The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685–691.10.1002/jmri.21049Search in Google Scholar

Jentzen, W., Freudenberg, L., Eising, E.G., Heinze, M., Brandau, W., and Bockisch, A. (2007). Segmentation of PET volumes by iterative image thresholding. J. Nucl. Med. 48, 108–114.Search in Google Scholar

Jernigan, T.L., Salmon, D.P., Butters, N., and Hesselink, J.R. (1991). Cerebral structure on MRI, Part II: specific changes in Alzheimer’s and Huntington’s diseases. Biol. Psychiatry 29, 68–81.10.1016/0006-3223(91)90211-4Search in Google Scholar

Johnson, P., Vandewater, L., Wilson, W., Maruff, P., Savage, G., Graham, P., Macaulay, L.S., Ellis, K.A., Szoeke, C., Martins, R.N., et al. (2014). Genetic algorithm with logistic regression for prediction of progression to Alzheimer’s disease. BMC Bioinform. 15(Suppl 16), S11.10.1186/1471-2105-15-S16-S11Search in Google Scholar PubMed PubMed Central

Joshi, S., Shenoy, D., Simha, V.G.G., Rrashmi, P.L., Venugopal, K.R., and Patnaik, L.M. (2010). Classification of Alzheimer’s disease and Parkinson’s disease by using machine learning and neural network methods. 2010 IEEE Second International Conference on Machine Learning and Computing (ICMLC) (Bangalore, India), pp. 218–222.10.1109/ICMLC.2010.45Search in Google Scholar

Kato, S., Homma, A., Sakuma, T., and Nakamura, M. (2015). Detection of mild Alzheimer’s disease and mild cognitive impairment from elderly speech: binary discrimination using logistic regression. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Milan, Italy), pp. 5569–5572.10.1109/EMBC.2015.7319654Search in Google Scholar PubMed

KavitaMahajan, M. and Rajput, M.S.M. (2012). A comparative study of ANN and SVM for EEG classification. Int. J. Eng. 1, 62–69.Search in Google Scholar

Kloppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack, C.R., Jr., Ashburner, J., and Frackowiak, R.S. (2008). Automatic classification of MR scans in Alzheimer’s disease. Brain 131, 681–689.10.1093/brain/awm319Search in Google Scholar PubMed PubMed Central

Konrad, C., Ukas, T., Nebel C., Arolt, V., Toga, A.W., and Narr, K.L. (2009). Defining the human hippocampus in cerebral magnetic resonance images – an overview of current segmentation protocols. Neuroimage 47, 1185–1195.10.1016/j.neuroimage.2009.05.019Search in Google Scholar PubMed PubMed Central

Kwon, M., Kavuri, S., and Lee, M. (2014). Action-perception cycle learning for incremental emotion recognition in a movie clip using 3D fuzzy GIST based on visual and EEG signals. Integr. Compu.-Aid. E. 21, 295–310.10.3233/ICA-140464Search in Google Scholar

Lanckriet, G.R., Deng, M., Cristianini, N., Jordan, M.I., and Noble, W.S. (2004). Kernel-based data fusion and its application to protein function prediction in yeast. Pac. Symp. Biocomput. 9, 300–311.10.1142/9789812704856_0029Search in Google Scholar

Laske, C., Leyhe, T., Stransky, E., Hoffmann, N., Fallgatter, A.J., and Dietzsch, J. (2011). Identification of a blood-based biomarker panel for classification of Alzheimer’s disease. Int. J. Neuropsychopharmacol. 14, 1147–1155.10.1017/S1461145711000459Search in Google Scholar

Lee, H.G., Yi, C.Y., Lee, D.E., and Arditi, D. (2015). An advanced stochastic time-cost tradeoff analysis based on a CPM-guided multi-objective genetic algorithm. Comput.-Aided Civ. Infrastruct. Eng. 30, 824–842.10.1111/mice.12148Search in Google Scholar

Leemput, V., Bakkour, A., Benner, T., Wiggins, G., Wald, L.L., Augustinack, J., Dickerson, B.C., Golland, P., and Fischl, B. (2009). Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus 19, 549–557.10.1002/hipo.20615Search in Google Scholar

Lester, H. and Arridge, S.R. (1999). A survey of hierarchical non-linear medical image registration. Pattern Recogn. 32, 129–149.10.1016/S0031-3203(98)00095-8Search in Google Scholar

Li, T. and Wang, Y. (2012). Multiscaled combination of MR and SPECT images in neuroimaging: a simplex method based variable-weight fusion. Compu. Methods Programs Biomed. 105, 31–39.10.1016/j.cmpb.2010.07.012Search in Google Scholar PubMed

Li, X., Wang, L., and Sung, E. (2008). AdaBoost with SVM-based component classifiers. Eng. Appl. Artif. Intell. 21, 785–795.10.1016/j.engappai.2007.07.001Search in Google Scholar

Liew, A.W. and Yan, H. (2003). An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans. Med. Imaging 22, 1063–1075.10.1109/TMI.2003.816956Search in Google Scholar PubMed

Liu, J.W. and Guo, L. (2015). Selection of initial parameters of K-means clustering algorithm for MRI brain image segmentation. 2015 IEEE International Conference on Machine Learning and Cybernetics (Guangzhou, China), pp. 123–127.10.1109/ICMLC.2015.7340909Search in Google Scholar

Lopez, M., Ramirez, J., Gorriz, J.M., Salas-Gonzalez, D., Alvarez, I., Segovia, F., and Chaves, R. (2009a). Multivariate approaches for Alzheimer’s disease diagnosis using Bayesian classifiers. 2009 IEEE Nuclear Science Symposium Conference Record (Orlando, FL, USA), pp. 3190–3193.Search in Google Scholar

Lopez, M., Ramirez, J., Gorriz, J.M., and Salas-Gonzalez, D. (2009b). Neurological image classification for the Alzheimer’s disease diagnosis using kernel PCA and support vector machines. IEEE Nuclear Science Symposium Conference Record (NSS/MIC) (Orlando, FL, USA), pp. 2486–2489.10.1109/NSSMIC.2009.5402069Search in Google Scholar

Mackay, S., Ezekiel, F., DiSclafani, V., Meyerhoff, D.J., Gerson, J., Norman, D., Fein, G., and Weiner, M.W. (1996). Alzheimer’s disease and subcortical ischemic vascular dementia: evaluation by combining MRI segmentation and 1H-MR spectroscopic imaging. Radiology 198, 537–545.10.1148/radiology.198.2.8596863Search in Google Scholar

Maintz, J.B.A. and Viergever, M.A. (1998). A survey of medical image registration. Med. Image Anal. 2, 1–36.10.1016/S1361-8415(01)80026-8Search in Google Scholar

Mariani, G., Bruselli, L., Kuwert, T., Kim, E.E., Flotats, A., Israel, O., Dondi, M., and Watanabe, N. (2010). A review on the clinical uses of SPECT/CT. Eur. J. Nucl. Med. Mol. Imaging 37, 1959–1985.10.1007/s00259-010-1390-8Search in Google Scholar PubMed

Martínez-Ballesteros, M., Bacardit, J., and Riquelme, J.C. (2015). Enhancing the scalability of evolutionary algorithms to discover quantitative association rules in large-scale datasets. Integr. Compu.-Aid. E. 22, 21–39.10.3233/ICA-140479Search in Google Scholar

Massachusetts General Hospital. (2013). Internet Brain Segmentation Repository. Available at: http://www.cma.mgh.harvard.edu/ibsr/. Accessed on May 2016.Search in Google Scholar

Matthews, P. and Jezzard, P. (2004). Functional magnetic resonance imaging. J. Neurol. Neurosurg. Psychiatry 75, 6–12.Search in Google Scholar

Mazzocco, T. and Hussain, A. (2012). Novel logistic regression models to aid the diagnosis of dementia. Expert Syst. Appl. 39, 3356–3361.10.1016/j.eswa.2011.09.023Search in Google Scholar

Meena, A. and Raja, K. (2012). K-means segmentation of Alzheimer’s disease in pet scan datasets – an implementation. International Conference on Advances in Signal Processing and Information Technology, Springer (Dubai), Vol. 117, pp. 158–162.Search in Google Scholar

Michalopoulos, K. and Bourbakis, N. (2015). Combining EEG microstates with fMRI structural features for modeling brain activity. Int. J. Neural Syst. 25, 1550041.10.1142/S0129065715500410Search in Google Scholar PubMed

Monno, L., Bellotti, R., Calvini, P., Monge, R., Frisoni, G.B., and Pievani, M. (2011). Hippocampal segmentation by random forest classification. 2011 IEEE International Workshop on Medical Measurements and Applications Proceedings (MeMeA) (Bari, Italy), pp. 536–539.10.1109/MeMeA.2011.5966763Search in Google Scholar

Morra, J.H., Tu, Z., Apostolova, L.G., Green, A.E., and Avedissian, C. (2008). Mapping hippocampal degeneration in 400 subjects with a novel automated segmentation approach. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (Dubai), pp. 336–339.10.1109/ISBI.2008.4541001Search in Google Scholar

Morra, J.H., Tu, Z., Apostolova, L.G., Green, A.E., Toga, A.W., and Thompson, P.M. (2010). Comparison of AdaBoost and support vector machines for detecting Alzheimer’s disease through automated hippocampal segmentation. IEEE Trans. Med. Imaging 29, 30–43.10.1109/TMI.2009.2021941Search in Google Scholar PubMed PubMed Central

Mueller, S.G., Weiner, M.W., Thal, L.J., Peterson, R.C., Jack, C.R., Jagust, W., Trojanowski, J.Q., Toga, A.W., and Beckett, L. (2005). Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimers Dement. 1, 55–66.10.1016/j.jalz.2005.06.003Search in Google Scholar PubMed PubMed Central

National Institutes of Health. (2015). National Institute of Neurological Disorders and Stroke. National Institutes of Health (Bethesda, MD). Available at: http://www.ninds.nih.gov/disorders/dementias/detail_dementia.htm. Accessed on May 2016.Search in Google Scholar

Nestor, S.M., Gibson, E., Gao, F., Kiss, A., and Black, S.E. (2013). A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer’s disease. Neuroimage 66, 50–70.10.1016/j.neuroimage.2012.10.081Search in Google Scholar PubMed PubMed Central

Oritz, A., Fajardo, D., Gorriz, J.M., Ramirez, J., and Martínez-Murcia, F.J. (2014). Multimodal image data fusion for Alzheimer’s disease diagnosis by sparse representation. Stud. Health Technol. Inform. 207, 11–18.Search in Google Scholar

Orru, G., Pettersson, W., Marquand, A.F., Sartori, G., and Mechelli, A. (2012). Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36, 1140–1152.10.1016/j.neubiorev.2012.01.004Search in Google Scholar PubMed

Paris, P.C.D., Pedrino, E.C., and Nicoletti, M.C. (2015). Automatic learning of image filters using Cartesian genetic programming. Integr. Compu.-Aid. E. 22, 135–151.10.3233/ICA-150482Search in Google Scholar

Payan, A. and Montana, G. (2015). Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. Comp. Vis. Patt. Recog. arXiv: 1502.02506.Search in Google Scholar

Peng, F. and Ouyang, Y. (2014). Optimal clustering of railroad track maintenance jobs. Comput.-Aided Civ. Infrastruct. Eng. 29, 235–247.10.1111/mice.12036Search in Google Scholar

Perez, G., Conci, A., Moreno, A.B., and Hernandez-Tamames, J.A. (2014). Rician noise attenuation in the wavelet packet transformed domain for brain MRI. Integr. Compu.-Aid. E. 21, 163–175.10.3233/ICA-130457Search in Google Scholar

Piaggi, P., Menicucci, D., Gentili, C., Handjaras, G., Gemignani, A., and Landi, A. (2014). Singular spectrum analysis and adaptive filtering enhance the functional connectivity analysis of resting state FMRI data. Int. J. Neural Syst. 24, 1450010.10.1142/S0129065714500105Search in Google Scholar PubMed

Pinheiro, P.R., Castro, A., and Pinheiro, M. (2008). A multicriteria model applied in the diagnosis of Alzheimer’s disease: a Bayesian network. 2008 11th IEEE International Conference on Computational Science and Engineering (Sao Paulo, Brazil), pp. 15–22.10.1109/CSE.2008.44Search in Google Scholar

Pluim, J.P.W., Maintz, J.B.A., and Viergever, M.A. (2003). Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imaging 22, 986–1004.10.1109/TMI.2003.815867Search in Google Scholar PubMed

Polikar, R., Tilley, C., Hillis, B., and Clark, C.M. (2010). Multimodal EEG, MRI and PET data fusion for Alzheimer’s disease diagnosis. Engineering in Medicine and Biology Society (EMBC). IEEE Annual International Conference of Engineering in Medicine and Biology Society (Buenos Aires, Argentina), pp. 6058–6061.10.1109/IEMBS.2010.5627621Search in Google Scholar PubMed

Ponz-Tienda, J.L., Pellicer, E., Benlloch-Marco, J., and Andrés-Romano, C. (2015). Fuzzy project scheduling problem with minimal generalized relations. Comput.-Aided Civ. Infrastruct. Eng. 30, 872–891.10.1111/mice.12166Search in Google Scholar

Prato, F.S., Thompson, R.T., Stodilka, R.Z., Marshall, H.R., Devito, T., Robertson, J.A., Thomas, A., and Théberge, J. (2011). Hybrid brain imaging with MRI/PET. IEEE General Assembly and Scientific Symposium (Istanbul, Turkey), pp. 1–4.10.1109/URSIGASS.2011.6051349Search in Google Scholar

Rangini, M. and Jiji, G.W. (2013). Detection of Alzheimer’s disease through automated hippocampal segmentation. 2013 International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s) (Kottayam, India), pp. 144–149.10.1109/iMac4s.2013.6526397Search in Google Scholar

Rao, A., Lee, Y., Gass, A., and Monsch, A. (2011). Classification of Alzheimer’s disease from structural MRI using sparse logistic regression with optional spatial regularization. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Boston, MA, USA), pp. 4499–4502.10.1109/IEMBS.2011.6091115Search in Google Scholar PubMed

Reyes, O., Morell, C., and Ventura, S. (2014). Evolutionary feature weighting to improve the performance of multi-label lazy algorithms. Integr. Compu.-Aid. E. 21, 339–354.10.3233/ICA-140468Search in Google Scholar

Rodrigues, P.M., Freitas, D., and Teixeira, J.P. (2012). Alzheimer electroencephalogram temporal events detection by K-means. Procedia Technol. 5, 859–864.10.1016/j.protcy.2012.09.095Search in Google Scholar

Rusinek, H., de Leon, M.J., George, A.E., Stylopoulos, L.A., Chandra, R., Smith, G., Rand, T., Mourino, M., and Kowalski, H. (1991). Alzheimer disease: measuring loss of cerebral gray matter with MRI imaging. Radiology 178, 109–114.10.1148/radiology.178.1.1984287Search in Google Scholar PubMed

Saba, L. (2015). Imaging in Neurodegenerative Disorders (UK: Oxford University Press).10.1093/med/9780199671618.001.0001Search in Google Scholar

Samant, A. and Adeli, H. (2000). Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis. Comput.-Aided Civ. Infrastruct. Eng. 15, 241–250.10.1111/0885-9507.00188Search in Google Scholar

Sankari, Z. (2011). EEG coherence and probabilistic neural networks for classification of Alzheimer’s disease. Alzheimers Dement. 7(Suppl), S175–S176.10.1016/j.jalz.2011.05.479Search in Google Scholar

Sankari, Z. and Adeli, H. (2011). Probabilistic neural networks for EEG-based diagnosis of Alzheimer’s disease using conventional and wavelet coherence. J. Neurosci. Methods 197, 165–170.10.1016/j.jneumeth.2011.01.027Search in Google Scholar PubMed

Sankari, Z., Adeli, H., and Adeli, A. (2011). Intrahemispheric, interhemispheric and distal EEG coherence in Alzheimer’s disease. Clin. Neurophysiol. 122, 897–906.10.1016/j.clinph.2010.09.008Search in Google Scholar PubMed

Sankari, Z., Adeli, H., and Adeli, A. (2012). Wavelet coherence model for diagnosis of Alzheimer’s disease. Clin. EEG Neurosci. 43, 268–278.10.1177/1550059412444970Search in Google Scholar PubMed

Scholkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Sung, K.K., Burges, C.J.C., Girosi, F., Niyogi, P., Poggio, T., et al. (1997). Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45, 2758–2765.10.1109/78.650102Search in Google Scholar

Sezgin, M. and Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13, 146–165.10.1117/1.1631315Search in Google Scholar

Shanthi, K.J. and Ravish, D.K. (2013). Image segmentation an early detection to Alzheimer’s disease. 2013 Annual IEEE India Conference (INDICON) (Mumbai, India).10.1109/INDCON.2013.6726006Search in Google Scholar

Shattuck, D.W., Sandor-Leahy, S.R., Schaper, K.A., Rottenberg, D.A., and Leahy, R.M. (2001), Magnetic resonance image tissue classification using a partial volume model. Neuroimage 13, 856–876.10.1006/nimg.2000.0730Search in Google Scholar PubMed

Shiee, N., Bazin, P.L., Ozturk, A., Reich, D.S., Calabresi, P.A., and Pham, D.L. (2010). A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage 49, 1524–1535.10.1016/j.neuroimage.2009.09.005Search in Google Scholar PubMed PubMed Central

Siddique, N. and Adeli, H. (2013). Computational intelligence – synergies of fuzzy logic. Neural Networks and Evolutionary Computing (West Sussex, UK: Wiley).Search in Google Scholar

Smith, S.M. (2002). Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155.10.1002/hbm.10062Search in Google Scholar PubMed PubMed Central

Smith-Vikos, T. and Slack, F.J. (2013). MicroRNAs circulate around Alzheimer’s disease. Genome Biol. 14, 125.10.1186/gb-2013-14-7-125Search in Google Scholar

Souplet, J.C., Lebrun, C., Ayache, N., and Malandain, G. (2008). An automatic segmentation of T2-FLAIR multiple sclerosis lesions. MIDAS J., MS Lesion Segmentation, MICCAI 2008 Workshop. http://hdl.handle.net/10380/1451.10.54294/6eyg0wSearch in Google Scholar

Subasi, A. and Gursoy, M.I. (2010). EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37, 8659–8666.10.1016/j.eswa.2010.06.065Search in Google Scholar

Sun, H. and Betti, R. (2015). A hybrid optimization algorithm with Bayesian inference for probabilistic model updating. Comput.-Aided Civ. Infrastruct. Eng. 30, 602–619.10.1111/mice.12142Search in Google Scholar

Tanabe, J.L., Amend, D., Schuff, N., DiSclafani, V., Ezekiel, F., Norman, D., Fein, G., and Weiner, M.W. (1997). Tissue segmentation of the brain in Alzheimer disease. AJNR Am. J. Neuroradiol. 18, 115–123.Search in Google Scholar

Tanchi, C., Theera-Umpon, N., and Auephanwiriyakul, S. (2012). Fully automatic brain segmentation for Alzheimer’s disease detection from magnetic resonance images. 2012 IEEE Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS) (Kobe, Japan), pp. 1393–1396.10.1109/SCIS-ISIS.2012.6505333Search in Google Scholar

Tandor, R., Adak, S., and Kaye, J. (2006). Neural networks for longitudinal studies in Alzheimer’s disease. Artif. Intell. Med. 36, 245–255.10.1016/j.artmed.2005.10.007Search in Google Scholar

Thatcher, R.W., Walker, R.A., Gerson, I., and Geisler, F.H. (1989). EEG discriminant analyses of mild head trauma. Electroencephalogr. Clin. Neurophysiol. 73, 94–106.10.1016/0013-4694(89)90188-0Search in Google Scholar

Torabi, M., Ardekani, R.D., and Fatemizadeh, E. (2006). Discrimination between Alzheimer’s disease and control group in MR-images based on texture analysis using artificial neural network. IEEE International Conference on Biomedical and Pharmaceutical Engineering (Singapore), pp. 79–83.Search in Google Scholar

Vahabi, Z., Amirfattahi, R., Ghassemi, F., and Shayegh, F. (2015). Online epileptic seizure prediction using wavelet-based bi-phase correlation of electrical signal tomography. Int. J. Neural Syst. 25, 1550028.10.1142/S0129065715500288Search in Google Scholar PubMed

Varghese, T., Sheela, K.R., Mathuranath, P.S., and Singh, A. (2012). Evaluation of different stages of Alzheimer’s disease using unsupervised clustering techniques and voxel based morphometry. World IEEE Congress on Information and Communication Technologies (WICT) (Trivandrum, India), pp. 953–958.10.1109/WICT.2012.6409212Search in Google Scholar

Vural, V. and Dy, J.G. (2004). A hierarchical method for multi-class support vector machines. ICML ’04 Proceedings of the Twenty-First International Conference on Machine Learning (Banff, Alberta, Canada), 105pp.10.1145/1015330.1015427Search in Google Scholar

Wang, Y., Resnick, S.M., Davatzikos, C., and the Baltimore Longitudinal Study of Aging and the Alzheimer’s Disease Neuroimaging Initiative. (2014). Analysis of spatio-temporal brain imaging patterns by hidden Markov models and serial MRI images. Hum. Brain Mapp. 35, 4777–4794.10.1002/hbm.22511Search in Google Scholar PubMed PubMed Central

Wang, H., Yajima, A., Liang, R.Y., and Castaneda-Lopez, H. (2015). Bayesian modeling of external corrosion in underground pipelines based on the integration of Markov chain Monte Carlo techniques and clustered inspection data. Comput.-Aided Civ. Infrastruct. Eng. 30, 300–316.10.1111/mice.12096Search in Google Scholar

Wu, J.W., Tseng, J.C.R., and Tsai, W.N. (2014). A hybrid linear text segmentation algorithm using hierarchical agglomerative clustering and discrete particle swarm optimization. Integr. Compu.-Aid. E. 21, 35–46.10.3233/ICA-130446Search in Google Scholar

Xhang, X., Hu, B., and Xu, L. (2015). Resting-state whole-brain functional connectivity networks for MCI classification using L2-regularized logistic regression. IEEE Trans. Nanobioscience 14, 237–247.10.1109/TNB.2015.2403274Search in Google Scholar PubMed

Yang, Y.B., Li, Y.N., Gao, Y., Yin, H. J., and Tang, Y. (2014). Structurally enhanced incremental neural learning for image classification with subgraph extraction. Int. J. Neural Syst. 24, 1450024.10.1142/S0129065714500245Search in Google Scholar PubMed

Zhang, Y. and Zhou, W. (2015). Multifractal analysis and relevance vector machine-based automatic seizure detection in intracranial. Int. J. Neural Syst. 25, 1550020.10.1142/S0129065715500203Search in Google Scholar PubMed

Zhang, Y., Brady, M., and Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57.10.1109/42.906424Search in Google Scholar PubMed

Zhang, D., Wang, Y., Zhou, L., Yuan, H., and Shen, D. (2011). Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55, 856–867.10.1016/j.neuroimage.2011.01.008Search in Google Scholar PubMed PubMed Central

Zhang, R., Xu, P., Guo, L., Zhang, Y., Li, P., and Yao, D. (2013). Z-score linear discriminant analysis for EEG based brain-computer interfaces. PLoS One 8, e74433.10.1371/journal.pone.0074433Search in Google Scholar PubMed PubMed Central

Received: 2016-5-5
Accepted: 2016-6-19
Published Online: 2016-8-12
Published in Print: 2016-12-1

©2016 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 25.4.2024 from https://www.degruyter.com/document/doi/10.1515/revneuro-2016-0029/html
Scroll to top button