Abstract
Alzheimer’s disease (AD) is an age-related neurodegenerative disease and the most common form of dementia. It is a brain disorder that impacts the daily life of the patient due to memory loss and cognitive changes. Due to population aging and the fact that dementia incidence rising sharply at ages greater than 75, AD has become a major public health problem. Currently, hippocampal atrophy assessed on structural magnetic resonance (MR) images is the most used imaging biomarker of AD. Among many methods applied for automated classification of cognitively normal (CN), mild cognitive impairment (MCI), and AD, the linear PCA projection method, also known as eigenbrain, has shown effectiveness in AD subject prediction even with its restriction of using only linear projections. This study investigates both linear (PCA) and non-linear (kernel PCA) projection method performances to classify 3-D structural MR images on the CN, MCI, and AD classes. Support vector machines (SVMs) were trained using feature vectors with different space dimensions obtained by projecting MR study images into the previously created “eigenbrain spaces.” We tested the method using different kernel functions for both cases, the eigenbrain projections and SVM classifiers. We also conducted our analyses separately using the whole-brain and the gray-matter (GM) regions. Comparison results between PCA and kernel PCA methods showed that non-linear projections improved the classification of MR images in both class groups, particularly when processing the GM region - CN \(\times \) MCI (PCA: AUC = 0.63 versus KPCA: AUC = 0.76), MCI \(\times \) DA (PCA: AUC = 0.67 versus KPCA: AUC = 0.74), and CN \(\times \) AD (PCA: AUC = 0.86 versus KPCA: AUC = 0.89).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdi, H., Williams, L.J.: Principal Component Analysis. Wiley Interdisc. Rev. Comput. Stat. 2(4), 433–459 (2010)
Ahmed, M.R., Zhang, Y., Feng, Z., Lo, B., Inan, O.T., Liao, H.: Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects. IEEE Rev. Biomed. Eng. 12, 19–33 (2018)
Alam, S., Kwon, G., Initiative, A.D.N.: Alzheimer disease classification using kpca, lda, and multi-kernel learning svm. Int. J. Imaging Syst. Technol. 27(2), 133–143 (2017)
Álvarez, I., et al.: Alzheimer’s diagnosis using eigenbrains and support vector machines. Electron. Lett. 45(7), 342–343 (2009)
Amoroso, N., et al.: Alzheimer’s disease diagnosis based on the hippocampal unified multi-atlas network (human) algorithm. Biomed. Eng. Online 17(1), 1–16 (2018)
Bassiony, H.S., Zickri, M.B., Metwally, H.G., Elsherif, H.A., Alghandour, S.M., Sakr, W.: Comparative histological study on the therapeutic effect of green tea and stem cells in Alzheimer’s disease complicating experimentally induced diabetes. Int. J. Stem Cells 8(2), 181–190 (2015)
Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)
Cao, P., et al.: Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures. Comput. Biol. Med. 91, 21–37 (2017)
Deture, M., Dickson, D.: The neuropathological diagnosis of Alzheimer’s disease. Mol. Neurodegeneration 14(32), 1–18 (2019)
Devanand, D.P., Bansal, R., Liu, J., Hao, X., Pradhaban, G., Peterson, B.S.: MRI hippocampal and entorhinal cortex mapping in predicting conversion to Alzheimer’s disease. Neuroimage 60(3), 1622–1629 (2012)
Elahi, F.M., Miller, B.L.: A clinicopathological approach to the diagnosis of dementia. Nat. Rev. Neurol. 13(8), 457–476 (2017)
Fawcett, T.: An introduction of ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Frisoni, G.B., Fox, N.C., Jack-Jr, C.R., Scheltens, P., Thompson, P.M.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67–77 (2010)
Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical Report, Department of Computer Science, National Taiwan University, Taiwan (May 2016). https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Iglesias, J.E., Liu, C.Y., Thompson, P.M., Tu, Z.: Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans. Med. Imaging 30(9), 1617–1634 (2011)
Jo, T., Nho, K., Saykin, A.: Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front. Aging Neurosci. 11(220), 1–14 (2019)
Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Phil. Trans. Royal Soc. Math. Phys. Eng. Sci. 374(2065), 1–16 (2016)
Juntu, J., Sijbers, J., Van Dyck, D., Gielen, J.: Bias Field Correction for MRI Images. In: Kurzyǹski, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol. 30, pp. 543-551. Springer, Berlin (2005) https://doi.org/10.1007/3-540-32390-2_64
Hett, K., Ta, V.-T., Manjón, J.V., Coupé, P.: Graph of hippocampal subfields grading for Alzheimer’s disease prediction. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 259–266. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_30
Kanghan, O., Young-Chul, C., Ko, W., Woo-Sung, K., Il-Seok, O.: Classifcation and visualization of Alzheimer’s disease using volumetric convolutional neural network and transfer learning. Sci. Reports 9(18150), 1–16 (2019)
Kelleher, J.D., Mac Namee, B., D’arcy, A.: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. 1 edn. MIT Press, Cambridge (2015)
Khedher, L., Ramirez, J., Gorriz, J.M., Brahim, A., Segovia, F., Initiative, A.D.N., et al.: 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)
Liu, J., Li, M., Lan, W., Wu, F., Pan, Y., Wang, J.: Classification of alzheimer’s disease using whole brain hierarchical network. IEEE Trans. Comput. Biol. Bioinf. 15(2), 624–632 (2018)
Liu, M., Zhang, D., Shen, D.: View-centralized multi-atlas classification for Alzheimer’s disease diagnosis. Hum. Brain Mapp. 36(5), 1847–1865 (2015)
Liu, M., Zhang, J., Nie, D., Yap, P.T., Shen, D.: Anatomical landmark based deep feature representation for MR images in brain disease diagnosis. IEEE J. Biomed. Health Inf. 22(5), 1476–1485 (2018)
Nell, C., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)
Nyúl, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)
Rehman, H.Z.U., Hwang, H., Lee, S.: Conventional and deep learning methods for skull stripping in brain MRI. Appl. Sci. 10(5), 1773 (2020)
Salvatore, C., Cerasa, A., Battista, P., Gilardi, M.C., Quattrone, A., Castiglioni, I.: Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. Front. Neurosci. 9, 307 (2015)
Sarwinda, D., Arymurthy, A.M.: Feature selection using kernel PCA for alzheimer’s disease detection with 3D MR images of brain. In: 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 329–333. IEEE, Bali, IDN (September 2013)
Schölkopf, B.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998)
Schölkopf, B., Smola, A., Müller, K.-R.: Kernel principal component analysis. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 583–588. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0020217
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Tustison, N.J., et al.: N4itk: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
World Health Organization: Dementia: A public health priority. Technical Report, World Health Organization, Geneva, Switzerland (2019)
Zhang, J., Liu, M., An, L., Gao, Y., Shen, D.: Alzheimer’s disease diagnosis using landmark-based features from longitudinal structural MR images. IEEE J. Biomed. Health Inf. 21(5), 1607–1616 (2017)
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)
Zhang, Y.D., Wang, S., Dong, Z.: Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog. Electromagnet. Res. 144, 171–184 (2014)
Zhang, Y., et al.: Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front. Comput. Neurosci. 9, 66 (2015)
Zhang, Y., Wang, S., Phillips, P., Yang, J., Yuan, T.F.: Three-dimensional eigenbrain for the detection of subjects and brain regions related with Alzheimer’s disease. J. Alzheimer’s Dis. 50(4), 1163–1179 (2016)
Acknowledgment
Funding for ADNI can be found at http://adni.loni.usc.edu/about/#fund-container.
Funding
This study was financed by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (grant numbers 2018/08826-9 and 2018/06049-5) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance Code 001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Araújo, M.R.M., Poloni, K.M., Ferrari, R.J. (2021). Assessment of Linear and Non-linear Feature Projections for the Classification of 3-D MR Images on Cognitively Normal, Mild Cognitive Impairment and Alzheimer’s Disease. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-86960-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86959-5
Online ISBN: 978-3-030-86960-1
eBook Packages: Computer ScienceComputer Science (R0)