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Analysis of Retinal OCT Images for the Early Diagnosis of Alzheimer’s Disease

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Modelling and Simulation in Science, Technology and Engineering Mathematics (MS-17 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 749))

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Abstract

Alzheimer disease (AD) is a cumulative brain disorder as well as irreversible neuronal disease that affects mostly the old age population. The investigation made on AD reveals that early symptoms of AD not only affect the brain but also the retina, especially on the Optical Coherence Tomography (OCT) images. For making an analysis using OCT images for diagnosing AD, an efficient and reliable technique should be developed with the help of advanced Biomedical methods on Engineering. The available brain imaging methods used for predicting AD is Positron Emission Tomography, Single Photon Emission Computed Tomography, and Magnetic Resonance Imaging. OCT is the most reliable retina imaging technique that can be used for diagnosing AD. In this regard, a scheme based on Wavelet Networks (WN) on OCT images for predicting AD at its earlier stage has been introduced. The WN uses mother wavelets and child wavelets for creating networks. This can be applied on OCT type images.

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References

  1. C.S. Sandeep, A.S. Kumar, A review on the early diagnosis of Alzheimer’s Disease (AD) through different tests, techniques and databases. AMSE J.–2015-Series: Modell. C. 76(1), 1–22 (2015)

    Google Scholar 

  2. C.S. Sandeep, A.S. Kumar, K. Mahadevan, P. Manoj, Feature extraction of MRI brain images for the early detection of Alzheimer ’s disease. Bioprocess. Eng. 1(2), 35–42 (2017). https://doi.org/10.11648/j.be.20170102.11

    Article  Google Scholar 

  3. C.S. Sandeep, A.S. Kumar, K. Mahadevan, P. Manoj, Dimensionality reduction of optical coherence tomography images for the early diagnosis of Alzheimer’s disease. Am. J. Electr. Electron. Eng. 5(2), 58–63 (2017). https://doi.org/10.12691/ajeee-5-2-4

    Article  Google Scholar 

  4. C.S. Sandeep, A.S. Kumar, A psychometric assessment method for the early diagnosis of Alzheimer’s disease. Int. J. Sci. Eng. Res.-IJSER. 8(3) (2017). (ISSN 2229-5518)

    Google Scholar 

  5. C.S. Sandeep, A.S. Kumar, “A review paper on the early diagnosis of Alzheimer’s Disease (AD) through profiling of human body parameters, Scientistlink, Coimbatore, India, 2013. Int. J. Comput. Sci. Eng. Commun. (IJCSEC), 1(1), 21–29 (2013)

    Google Scholar 

  6. M.P. Frosch, D.C. Anthony, U.D. Girolami, The central nervous system, in Robbins and Cotran Pathologic Basis of Disease, ed. by S.L. Robbins, V. Kumar, A.K. Abbas, R.S. Cotran, N. Fausto (Elsevier srl, Philadelphia, 2010), pp. 1313–1317. ISBN-10: 1416031219

    Chapter  Google Scholar 

  7. R.A. Harvey, P.C. Champe, B.D. Fisher, Lippincott’s illustrated reviews: microbiology, 2nd edn. (Lippincott Williams and Wilkins, 2006), p. 432. ISBN-10: 0781782155

    Google Scholar 

  8. J.C. Blanks, S.Y. Schmidt, Y. Torigoe, K.V. Porrello, D.R. Hinton, R.H. Blanks, Retinal pathology in Alzheimer’s disease. II. Regional neuron loss and glial changes in GCL. Neurobiol. Aging 17, 385–395 (1996)

    Article  Google Scholar 

  9. J.C. Blanks, Y. Torigoe, D.R. Hinton, R.H. Blanks, Retinal pathology in Alzheimer’s disease. I. Ganglion cell loss in foveal/parafoveal retina. Neurobiol. Aging 17, 377–384 (1996)

    Article  Google Scholar 

  10. D.R. Hinton, A.A. Sadun, J.C. Blanks, C.A. Miller, Optic-nerve degeneration in Alzheimer’s disease. N. Engl. J. Med. 315, 485–487 (1986)

    Article  Google Scholar 

  11. A.A. Sadun, C.J. Bassi, Optic nerve damage in Alzheimer’s disease. Ophthalmology 97, 9–17 (1990)

    Article  Google Scholar 

  12. R.M. Cohen, K. Rezai-Zadeh, T.M. Weitz, A. Rentsendorj, D. Gate, I. Spivak et al., A transgenic Alzheimer rat with plaques, tau pathology, behavioral impairment, oligomeric abeta, and frank neuronal loss. J. Neurosci. 33, 6245–6256 (2013)

    Article  Google Scholar 

  13. M. Koronyo-Hamaoui, Y. Koronyo, A.V. Ljubimov, C.A. Miller, M.K. Ko, K.L. Black et al., Identification of amyloid plaques in retinas from Alzheimer’s patients and noninvasive in vivo optical imaging of retinal plaques in a mouse model. Neuroimage. 54(1), S204–S217 (2011)

    Article  Google Scholar 

  14. B. Liu, S. Rasool, Z. Yang, C.G. Glabe, S.S. Schreiber, J. Ge et al., Amyloid-peptide vaccinations reduce β-amyloid plaques but exacerbate vascular deposition and inflammation in the retina of Alzheimer’s transgenic mice. Am. J. Pathol. 175, 2099–2110 (2009)

    Article  Google Scholar 

  15. A. Ning, J. Cui, E. To, K.H. Ashe, J. Matsubara, Amyloid-beta deposits lead to retinal degeneration in a mouse model of Alzheimer disease. Invest. Ophthalmol. Vis. Sci. 49, 5136–5143 (2008)

    Article  Google Scholar 

  16. S.E. Perez, S. Lumayag, B. Kovacs, E.J. Mufson, S. Xu, Beta-amyloid deposition and functional impairment in the retina of the APPswe/PS1DeltaE9 transgenic mouse model of Alzheimer’s disease. Invest. Ophthalmol. Vis. Sci. 50, 793–800 (2009). https://doi.org/10.1167/iovs.08-2384

    Article  Google Scholar 

  17. C.A. Curcio, D.N. Drucker, Retinal ganglion cells in Alzheimer’s disease and aging. Ann. Neurol. 33, 248–257 (1993). https://doi.org/10.1002/ana.410330305

    Article  Google Scholar 

  18. D.C. Davies, P. McCoubrie, B. McDonald, K.A. Jobst, Myelinated axon number in the optic nerve is unaffected by Alzheimer’s disease. Br. J. Ophthalmol. 79, 596–600 (1995)

    Article  Google Scholar 

  19. V. Parisi, R. Restuccia, F. Fattapposta, C. Mina, M.G. Bucci, F. Pierelli, Morphological and functional retinal impairment in Alzheimer’s disease patients. Clin. Neurophysiol. 112, 1860–1867 (2001)

    Article  Google Scholar 

  20. M.L. Monteiro, L.P. Cunha, L.V. Costa-Cunha, O.O. Maia Jr., M.K. Oyamada, Relationship between optical coherence tomography, pattern electroretinogram and automated perimetry in eyes with temporal hemianopia from chiasmal compression. Invest. Ophthalmol. Vis. Sci. 50, 3535–3541 (2009)

    Article  Google Scholar 

  21. M.L. Monteiro, D.B. Fernandes, S.L. Apostolos-Pereira, D. Callegaro, Quantification of retinal neural loss in patients with neuromyelitis optica and multiple sclerosis with or without optic neuritis using Fourier-domain optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 53, 3959–3966 (2012)

    Article  Google Scholar 

  22. M.L. Monteiro, C.L. Afonso, Macular thickness measurements with frequency domain-OCT for quantification of axonal loss in chronic papilledema from pseudotumor cerebri syndrome. Eye 28, 390–398 (2014)

    Article  Google Scholar 

  23. K.-S. Cheng, J.-S. Lin, C.-W. Mao, Techniques and comparative analysis of neural network systems and fuzzy systems in medical image segmentation. Fuzzy Theor. Syst. Tech. Appl. 3, 973–1008 (1999)

    Article  Google Scholar 

  24. J. Jiang, P. Trundle, J. Ren, Medical image analysis with artificial neural networks. Comput. Med. Imag. Graph. 34(8), 617–631 (2010)

    Article  Google Scholar 

  25. R.M. Balabin, R.Z. Safieva, E.I. Lomakina, Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra. J. Chemometr. Intell. Lab. Syst. 93(1), 58–62 (2008)

    Article  Google Scholar 

  26. Q. Zhang, A. Benveniste, Wavelet networks. IEEE Trans. Neural Netw. 3(6), 889–898 (1992)

    Article  Google Scholar 

  27. Y.C. Pati, P.S. Krishnaprasad, Analysis and synthesis of feedforward neural networks using discrete affinewavelet transformations. IEEE Trans. Neur. Netw. 4(1), 73–85 (1992)

    Article  Google Scholar 

  28. H.H. Szu, B.A. Telfer, S.L. Kadambe, Neural network adaptive wavelets for signal representation and classification. Opt. Eng. 31(9), 1907–1916 (1992)

    Article  Google Scholar 

  29. H. Zhang, B. Zhang, W. Huang, Q. Tian, Gabor wavelet associative memory for face recognition. IEEE Trans. Neural Netw. 16(1), 275–278 (2005)

    Article  Google Scholar 

  30. O. Jemai, M. Zaied, C.B. Amar, M.A. Alimi, Pyramidal hybrid approach: wavelet network with OLS algorithm-based image classification. Int. J. Wavel. Multir. Inf. Process. 9(1), 111–130 (2011)

    Article  MathSciNet  Google Scholar 

  31. R. Galvao, V.M. Becerra, M.F. Calado, Linear–wavelet networks. Int. J. Appl. Math. Comput. Sci. 14(2), 221–232 (2004)

    MathSciNet  MATH  Google Scholar 

  32. S.A. Billings, H.L. Wei, A new class of wavelet networks for nonlinear system identification. IEEE Trans. Neural Netw. 16(4), 862–874 (2005)

    Article  Google Scholar 

  33. J. Gonzalez-Nuevo, F. Argueso, M. Lopez-Caniego, L. Toffolatti, J.L. Sanz, P. Vielva, D. Herranz, The mexican hat wavelet family application to point source detection in CMB maps. Mon. Not. Roy. Astron. Soc. 369, 1603–1610 (2006)

    Article  Google Scholar 

  34. Y. Oussar, G. Dreyfus, Initialization by selection for wavelet network training. Neurocomputing 34(1), 131–143 (2000)

    Article  Google Scholar 

  35. R. Baron, B. Girau, Parameterized normalization: application to wavelet networks. Proc. IEEE Int. Conf. Neural Netw. 2, 1433–1437 (1998)

    Article  Google Scholar 

  36. Q.H. Zhang, Using wavelet network in nonparametric estimation. IEEE Trans. Neural Netw. 8(2), 227–236 (1997)

    Article  Google Scholar 

  37. M. Davanipoor, M. Zekri, F. Sheikholeslam, Fuzzy wavelet neural network with an accelerated hybrid learning algorithm. IEEE Trans. Fuzzy Syst. 20(3), 463–470 (2012)

    Article  Google Scholar 

  38. F. Mokhtarian, S. Abbasi, Shape similarity retrieval under affine transforms. Pattern Recognit. 35(1), 31–41 (2002)

    Article  Google Scholar 

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Acknowledgements

The authors in this research work are very much thankful to SGM & RF, Trivandrum, India for the support for conducting the study and providing the required dataset. The authors are also thankful to the Institutional Ethics Committee Sree Gokulam Medical College & Research Foundation standard operating Procedures (SGMC-IEC: SOPS) members, Dr.V Mohanan Nair (Chairman), Dr. Regi Jose (Member Secretary IEC), Dr. K K Manojan (Member, Institution Review Board (IRB), IEC), Dr. P.Sivasankarapillai (Chairman IRB) and Dr. Jeesha C Haran (Secretary IRB) for giving the permission for the study.

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Authors and Affiliations

  1. Department of ECE, College of Engineering, Trivandrum, Kerala, India

    C. S. Sandeep & A. Sukesh Kumar

  2. Department of Ophthalmology, SGMC&RF, Trivandrum, India

    K. Mahadevan

  3. Department of Neurology, SGMC&RF, Trivandrum, India

    P. Manoj

Authors
  1. C. S. Sandeep
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  2. A. Sukesh Kumar
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  3. K. Mahadevan
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  4. P. Manoj
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Corresponding author

Correspondence to C. S. Sandeep .

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Editors and Affiliations

  1. Department of Electrical Engineering, Ghani Khan Choudhury Institute of Engineering and Technology, Malda, West Bengal, India

    Surajit Chattopadhyay

  2. Department of Electrical Engineering, MCKV Institute of Engineering, Howrah, West Bengal, India

    Tamal Roy

  3. University of Calcutta, Kolkata, West Bengal, India

    Samarjit Sengupta

  4. University of Lyon, Lyon, France

    Christian Berger-Vachon

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Sandeep, C.S., Sukesh Kumar, A., Mahadevan, K., Manoj, P. (2019). Analysis of Retinal OCT Images for the Early Diagnosis of Alzheimer’s Disease. In: Chattopadhyay, S., Roy, T., Sengupta, S., Berger-Vachon, C. (eds) Modelling and Simulation in Science, Technology and Engineering Mathematics. MS-17 2017. Advances in Intelligent Systems and Computing, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-74808-5_43

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  • DOI: https://doi.org/10.1007/978-3-319-74808-5_43

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