Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging

Yasuo Yamashita, Hidetaka Arimura, Takashi Yoshiura, Chiaki Tokunaga, Ohara Tomoyuki, Koji Kobayashi, Yasuhiko Nakamura, Nobuyoshi Ohya, Hiroshi Honda, Fukai Toyofuku

Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Division of Radiology, Department of Medical Technology, Kyusyu University Hospital, Fukuoka, Japan.
Faculty of Medical Science, Kyushu University, Fukuoka, Japan.
Graduate School of Medical Science, Kyushu University, Fukuoka, Japan Division of Radiology, Department of Medical Technology, Kyusyu University Hospital, Fukuoka, Japan.

DOI: 10.4236/jbise.2013.611137                

Abstract

Alzheimer’s disease (AD) is a dementing disorder and one of the major public health problems in countries with greater longevity. The cerebral cortical thickness and cerebral blood flow (CBF), which are considered as morphological and functional image features, respectively, could be decreased in specific cerebral regions of patients with dementia of Alzheimer type. Therefore, the aim of this study was to develop a computer-aided classification system for AD patients based on machine learning with the morphological and functional image features derived from a magnetic resonance (MR) imaging system. The cortical thicknesses in ten cerebral regions were derived as morphological features by using gradient vector trajectories in fuzzy membership images. Functional CBF maps were measured with an arterial spin labeling technique, and ten regional CBF values were obtained by registration between the CBF map and Talairach atlas using an affine transformation and a free form deformation. We applied two systems based on an arterial neural network (ANN) and a support vector machine (SVM), which were trained with 4 morphological and 6 functional image features, to 15 AD patients and 15 clinically normal (CN) subjects for classification of AD. The area under the receiver operating characteristic curve (AUC) values for the two systems based on the ANN and SVM with both image features were 0.901 and 0.915, respectively. The AUC values for the ANN-and SVM-based systems with the morphological features were 0.710 and 0.660, respectively, and those with the functional features were 0.878 and 0.903, respectively. Our preliminary results suggest that the proposed method may have potential for assisting radiologists in the differential diagnosis of AD patients by using morphological and functional image features.

Keywords

Computer-aided Classification (CAD); Alzheimer’s Disease; Magnetic Resonance Imaging (MRI); Arterial Spin Labeling (ASL); Fuzzy Membership Image; Cortical Thickness; Cerebral Blood Flow (CBF)

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	World Health Organization (2006) The World Health Report 2006, Working Together for Health. http://www.who.int/whr/2006/en/index.html
[2]	Chris, H., Vikas, S., Guofan, X. and Sterling C.J. (2011) The Alzheimer’s disease neuroimaging initiative, predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population. NeuroImage, 55, 574-589. http://dx.doi.org/10.1016/j.neuroimage.2010.10.081
[3]	Catriona, D.M., David, C., Stephen P.M. and A. Peter P. (2001) Risk factors for dementia. Advances in Psychiatric Treatment, 7, 24-31. http://dx.doi.org/10.1192/apt.7.1.24
[4]	Yoshitake, T., Kiyohara, Y., Kato, I., Ohmura, T., Iwamoto, H., Nakayama, K., Ohmori, S., Nomiyama, K., Kawano, H., Ueda, K., Sueishi, K., Tsuneyoshi, M. and Fujishima, M. (1995) Incidence and risk factors of vascular dementia and Alzheimer’s disease in a defined elderly Japanese population. Neurology, 45, 1161-1168. http://dx.doi.org/10.1212/WNL.45.6.1161
[5]	Claire, M. and Christian, D. (2006) Alzheimer disease: Progress or profit? Nature Medicine, 12, 780-784. http://dx.doi.org/10.1038/nm0706-780
[6]	Bronge, L., Bogdanovic, N. and Wahlund, L.O. (2002) Postmortem MRI and histopathology of white matter changes in Alzheimer brains: A quantitative, comparative study. Dementia and Geriatric Cognitive Disorders, 13, 205-212. http://dx.doi.org/ 10.1159/000057698
[7]	Brun, A. and Englund, E. (1986) A white matter disorder in dementia of the Alzheimer type: A pathoanatomical study. Annals of Neurology, 19, 253-262. http://dx.doi.org/10.1002/ana.410190306
[8]	Englund, E. (1998) Neuropathology of white matter changes in Alzheimer’s disease and vascular dementia. Dementia and Geriatric Cognitive Disorders, 9, 6-12. http://dx.doi.org/10.1159/000051183
[9]	Agosta, F., Pievani, M., Sala, S., Geroldi, C., Galluzzi, S., Frisoni, G.B. and Filippi, M., (2011) White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology, 258, 853-863. http://dx.doi.org/10.1148/radiol.10101284/-/DC1
[10]	Matsuda, H. (2007) Cerebral blood flow and metabolic abnormalities in Alzheimer’s disease. Annals of Nuclear Medicine, 15, 85-92. http://dx.doi.org/10.1007/BF02988596
[11]	Hirata, Y., Matsuda, H. and Nemoto, K. (2005) Voxelbased morphometry to discriminate early Alzheimer’s disease from controls. Neuroscience Letter, 382, 269-274. http://dx.doi.org/10.1016/j.neulet.2005.03.038
[12]	Matsuda, H. (2007) Role of neuroimaging in Alzheimer’s disease, with emphasis on brain perfusion SPECT. Journal of the Nuclear Medicine, 48, 1289-1300. http://dx.doi.org/10.2967/jnumed.106.037218
[13]	Li, S., Shi, F., Pu, F., Li, X., Jiang, T., Xie, S. and Wang, Y. (2007) Hippocampal shape analysis of Alzheimer disease based on machine learning methods. American Journal of Neuroradiology, 28, 1339-1345. http://dx.doi.org/10.3174/ajnr.A0620
[14]	Petersen, E.T., Zimine, I., Ho, Y.C. and Golay, X. (2006) Non-invasive measurement of perfusion: A critical review of arterial spin labelling techniques. British Journal of Radiology, 79, 688-701. http://dx.doi.org/10.1259/bjr/67705974
[15]	Kim, S.G. and Tsekos, N.V. (1997) Perfusion imaging by a flow-sensitive alternating inversion recovery (FAIR) technique: Application to functional brain imaging. Magnetic Resonance in Medicine, 37, 425-35. http://dx.doi.org/10.1002/mrm.1910370321
[16]	Yoshiura, T., Hiwatashi, A., Noguchi, T., Yamashita, K., Ohyagi, Y., Monji, A., Nagao, E., Kamano, H., Togao, O. and Honda, H. (2009) Arterial spin labelling at 3-T MR imaging for detection of individuals with Alzheimer’s disease. European Radiology, 19, 2819-2825. http://dx.doi.org/10.1007/s00330-009-1511-6
[17]	Arimura, H., Yoshiura, T., Kumazawa, S., Tanaka, K., Koga, H., Mihara, F., Honda, H., Sakai, S., Toyofuku, F. and Higashida, Y. (2008) Automated method for identification of patients with Alzheimer’s disease based on three-dimensional MR images. Academic Radiology, 15, 274-284. http://dx.doi.org/10.1016/j.acra.2007.10.020
[18]	Kloppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack, C.R., Ashburner, Jr, J. and Frackowiak, R.S.J. (2008) Automatic classification of MR scans in Alzheimer’s disease. Brain, 131, 681-689. http://dx.doi.org/10.1093/brain/awm319
[19]	Colliot, O., Chételat, G., Chupin, M., Desgranges, B., Magnin, B., Benali, H., Dubois, B., Garnero, L., Eustache, F. and Lehéricy, S. (2008) Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology, 248, 194-201. http://dx.doi.org/10.1148/radiol.2481070876
[20]	Ramírez, J., Górriz, J.M., Segovia, F., Chaves, R., SalasGonzalez, D., López, M., álvarez, I. and Padilla, P. (2010) Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification. Neuroscience Letters, 472, 99-103. http://dx.doi.org/10.1016/j.neulet.2010.01.056
[21]	Tokunaga, C., Arimura, H., Yoshiura, T., Ohara, T., Yamashita, Y., Kobayashi, K., Magome, T., Nakamura, Y., Honda, H., Hirata, H., Ohki, M. and Toyofuku, F. (2013) Automated measurement of three-dimensional cerebral cortical thickness in Alzheimer’s patients using localized gradient vector trajectory in fuzzy membership maps. Journal of Biomedical Science and Engineering, 6, 327-336. http://dx.doi.org/10.4236/jbise.2013.63A042
[22]	Petersen, E.T., Lim, T. and Golay, X. (2006) Model-free arterial spin labeling quantification approach for perfusion MRI. Magnetic Resonance in Medicine, 55, 219-232. http://dx.doi.org/10.1002/mrm.20784
[23]	Talairach, J. and Tournoux, P. (1988) Co-planar stereotaxic atlas of the human brain: 3-Dimensional proportional system: An approach to cerebral imaging. Thieme Medical Publishers, Inc., New York.
[24]	Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A. and Fox, P.T. (2000) Automated Talairach Atlas labels for functional brain mapping. Human Brain Mapping, 1, 120-131. http://dx.doi.org/10.1002/1097-0193(200007)10:3<120::AID-HBM30>3.0.CO;2-8
[25]	Lancaster, J.L., Rainey, L.H., Summerlin, J.L., Freitas, C.S., Fox, P.T., Evans, A.C., Toga, A.W. and Mazziotta J.C. (1997) Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human Brain Mapping, 5, 238-242. http://dx.doi.org/10.1002/(SICI)1097-0193(1997)5:4<238::AID-HBM6>3.0.CO;2-4
[26]	Lee, S., Wolberg, G. and Shin, S.Y. (1997) Scattered data interpolation with multilevel B-splines. IEEE Transactions on Visualization and Computer Graphics, 3, 228-244. http://dx.doi.org/10.1109/2945.620490
[27]	Laboratory of Neuro Imaging (2012) International consortium for brain mapping. http://www.loni.ucla.edu/ICBM/
[28]	Alsop, D.C., Detre, J.A. and Grossman, M. (2000) Assessment of cerebral blood flow in Alzheimer’s disease by spinlabeled magnetic resonance imaging. Annals of Neurology, 47, 93-100. http://dx.doi.org/10.1002/1531-8249(200001)47:1<93::AID-ANA15>3.0.CO;2-8
[29]	Joachims, T. (2008) SVMlight, Cornell University. http://svmlight.joachims.org/
[30]	Metz, C.E., Herman, B.A. and Roe, C.A. (1998) Statistical comparison of two ROC curve estimates obtained from partially-paired datasets. Medical Decision Making, 18, 110-121. http://dx.doi.org/10.1177/0272989X9801800118
[31]	Wua, T.K., Huangb, S.C. and Mengc, Y.R. (2008) Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities. Expert Systems with Applications, 34, 1846-1856. http://dx.doi.org/10.1016/j.eswa.2007.02.026