American Journal of Electrical and Electronic Engineering. 2017, 5(2), 58-63
DOI: 10.12691/AJEEE-5-2-4
Original Research

Dimensionality Reduction of Optical Coherence Tomography Images for the Early Diagnosis of Alzheimer’s Disease

Sandeep C S1, , Sukesh Kumar A1, K Mahadevan2 and Manoj P3

1Department of Electronics and Communication, College of Engineering, Trivandrum, India

2Department of Ophthalmology, Sree Gokulam Medical College and Research Foundation, Trivandrum, India

3Department of Neurology, Sree Gokulam Medical College and Research Foundation, Trivandrum, India

Pub. Date: April 27, 2017

Cite this paper

Sandeep C S, Sukesh Kumar A, K Mahadevan and Manoj P. Dimensionality Reduction of Optical Coherence Tomography Images for the Early Diagnosis of Alzheimer’s Disease. American Journal of Electrical and Electronic Engineering. 2017; 5(2):58-63. doi: 10.12691/AJEEE-5-2-4

Abstract

Alzheimer’s disease (AD) is the most common cause of dementia and its incidence is increasing worldwide along with population aging. Previous clinical and histologic studies suggest that the neurodegenerative process, which affects the brain, may also affect the retina of AD patients. In this case, the employment of advanced Biomedical Engineering Technology will definitely helpful for making diagnosis. Any disease modifying treatments which are developed are most possibly to be achieving success if initiated early in the process, and this needs that we tend to develop reliable, validated and economical ways to diagnose Alzheimer’s kind pathology. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. Profiling of human body parameter using computers can be utilised for the early Diagnosis of Alzheimer’s disease. There are lot of tests and imaging modalities to be performed for an effective Diagnosis of the disease. Prominent of them are Magnetic Resonance Imaging Scan (MRI), Positron Emission Tomography (PET), Single Photon Emission CT Scanning (SPECT) and Optical Coherence Tomography (OCT).In the recent studies made on Alzheimer’s disease it is clearly investigated that are some parameter changes on the retina of the eye of the AD patients. In this research we have proposed a new scheme based on Wavelet Networks (WN) for the dimensionality reduction of OCT retinal images for the early Diagnosis of AD.

Keywords

Alzheimer’s disease, OCT, early diagnosis, wavelons

Copyright

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]  Sandeep C S, Sukesh Kumar A, A Review on the Early Diagnosis of Alzheimer’s Disease (AD) through Different Tests, Techniques and Databases AMSE JOURNALS -2015-Series: Modelling C; Vol. 76; N° 1; pp 1-22.
 
[2]  Sandeep C S, Sukesh Kumar A, Susanth M J, “The Online Datasets Used to Classify the Different Stages for the Early Diagnosis of Alzheimer’s Disease (AD)”, International Journal of Engineering and Advanced Technology, Volume-6 Issue-4, April 2017: 38-45.
 
[3]  Sandeep C S, Sukesh Kumar A, A Psychometric Assessment Method for the Early Diagnosis of Alzheimer’s disease”, International Journal of Scientific & Engineering Research -IJSER, Volume 8 Issue 3 -MARCH 2017.
 
[4]  Sandeep C S, Sukesh Kumar A, Susanth M J "The Early Diagnosis of Alzheimer Disease (AD) Using CAMD, TREAD and NAAC Databases" International Journal for Science and Advance Research In Technology, IJSART - Volume 3 Issue 3 -MARCH 2017: 366-371.
 
[5]  Sandeep C.S, Sukesh Kumar.A, “A Review Paper on the Early Diagnosis of Alzheimer’s Disease(AD) through Profiling of Human Body Parameters”, Scientistlink, Coimbatore, India, 2013, International Journal of Computer Science and Engineering Communications (IJCSEC), Vol.1 Issue.1, pp. 21-29, December 2013.
 
[6]  Frosch, M.P., D.C. Anthony and U.D. Girolami, 2010. The Central Nervous System. In: Robbins and Cotran Pathologic Basis of Disease, Robbins, S.L., V. Kumar, A.K. Abbas, R.S. Cotran and N. Fausto (Eds.), Elsevier srl, Philadelphia, pp: 1313-1317.
 
[7]  Harvey, R.A., P.C. Champe, B.D. Fisher, Lippincott’s Illustrated Reviews: Microbiology. 2nd Edn., Lippincott Williams and Wilkins, pp: 432, 2006.
 
[8]  Blanks JC, Schmidt SY, Torigoe Y, Porrello KV, Hinton DR, Blanks RH. Retinal pathology in Alzheimer’s disease. II. Regional neuron loss and glial changes in GCL. Neurobiol Aging. 1996; 17: 385-395.
 
[9]  Blanks JC, Torigoe Y, Hinton DR, Blanks RH. Retinal pathology in Alzheimer’s disease. I. Ganglion cell loss in foveal/parafoveal retina. Neurobiol Aging. 1996; 17: 377-384.
 
[10]  Hinton DR, Sadun AA, Blanks JC, Miller CA. Optic-nerve degeneration in Alzheimer’s disease. N Engl J Med. 1986; 315: 485-487.
 
[11]  Sadun AA, Bassi CJ. Optic nerve damage in Alzheimer’s disease. Ophthalmology. 1990; 97: 9-17.
 
[12]  Cohen RM, Rezai-Zadeh K, Weitz TM, Rentsendorj A, Gate D, Spivak I, et al. A transgenic Alzheimer rat with plaques, tau pathology, behavioral impairment, oligomeric abeta, and frank neuronal loss. J Neurosci. 2013; 33: 6245-6256.
 
[13]  Koronyo-Hamaoui M, Koronyo Y, Ljubimov AV, Miller CA, Ko MK, Black KL, 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. 2011; 54 (Suppl. 1): S204-S217.
 
[14]  Liu B, Rasool S, Yang Z, Glabe CG, Schreiber SS, Ge J, 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. 2009; 175: 2099-2110.
 
[15]  Ning A, Cui J, To E, Ashe KH, Matsubara J. Amyloid-beta deposits lead to retinal degeneration in a mouse model of Alzheimer disease. Invest Ophthalmol Vis Sci. 2008; 49: 5136-5143.
 
[16]  Perez SE, Lumayag S, Kovacs B, Mufson EJ, Xu S. Beta-amyloid deposition and functional impairment in the retina of the APPswe/PS1DeltaE9 transgenic mouse model of Alzheimer’s disease. Invest Ophthalmol Vis Sci. 2009; 50: 793-800.
 
[17]  Curcio CA, Drucker DN. Retinal ganglion cells in Alzheimer’s disease and aging. Ann Neurol. 1993; 33: 248-257.
 
[18]  Davies DC, McCoubrie P, McDonald B, Jobst KA. Myelinated axon number in the optic nerve is unaffected by Alzheimer’s disease. Br J Ophthalmol. 1995; 79: 596-600.
 
[19]  Parisi V, Restuccia R, Fattapposta F, Mina C, Bucci MG, Pierelli F. Morphological and functional retinal impairment in Alzheimer’s disease patients. Clin Neurophysiol. 2001; 112: 1860-1867.
 
[20]  Monteiro ML, Cunha LP, Costa-Cunha LV, Maia OO, Jr, Oyamada MK. Relationship between optical coherence tomography, pattern electroretinogram and automated perimetry in eyes with temporal hemianopia from chiasmal compression. Invest Ophthalmol Vis Sci. 2009; 50: 3535-3541.
 
[21]  Monteiro ML, Fernandes DB, Apostolos-Pereira SL, Callegaro D. 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. 2012; 53: 3959-3966.
 
[22]  Monteiro ML, Afonso CL. Macular thickness measurements with frequency domain-OCT for quantification of axonal loss in chronic papilledema from pseudotumor cerebri syndrome. Eye. 2014; 28: 390-398.
 
[23]  K.-S. Cheng, J.-S. Lin, and C.-W. Mao, “Techniques and comparative analysis of neural network systems and fuzzy systems in medical image segmentation,” Fuzzy Theor. Syst. Tech. Appl., vol. 3, pp. 973-1008, 1999.
 
[24]  J. Jiang, P. Trundle, and J. Ren, “Medical image analysis with artificial neural networks,” Comput. Med. Imag. Graph., vol. 34, no. 8, pp. 617-631, Dec. 2010.
 
[25]  R. M. Balabin,R. Z. Safieva, and E. I. Lomakina,“Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra,” J. Chemometr. Intell. Lab. Syst., vol. 93, no. 1, pp. 58-62, Aug. 2008.
 
[26]  Q. Zhang and A. Benveniste, “Wavelet networks,” IEEE Trans. Neural Netw., vol. 3, no. 6, pp. 889-898, Nov. 1992.
 
[27]  Y. C. Pati and P. S. Krishnaprasad, “Analysis and synthesis of feedforward neural networks using discrete affinewavelet transformations,” IEEE Trans. Neur. Netw., vol. 4, no. 1, pp. 73-85, Jan. 1992.
 
[28]  H. H. Szu, B. A. Telfer, and S. L. Kadambe, “Neural network adaptive wavelets for signal representation and classification,” Opt. Eng., vol. 31, no. 9, pp. 1907-1916, Sep. 1992.
 
[29]  H. Zhang, B. Zhang, W. Huang, and Q. Tian, “Gabor wavelet associative memory for face recognition,” IEEE Trans. Neural Netw., vol. 16, no. 1, pp. 275-278, Jan. 2005.
 
[30]  O. Jemai, M. Zaied, C. B. Amar, and M. A. Alimi, “Pyramidal hybrid approach: Wavelet network with OLS algorithm-based image classification,” Int. J. Wavel. Multir. Inf. Process., vol. 9, no. 1, pp. 111-130, Mar. 2011.
 
[31]  R. Galvao, V. M. Becerra, and M. F. Calado, “Linear-wavelet networks,” Int. J. Appl. Math. Comput. Sci., vol. 14, no. 2, pp. 221-232, Aug. 2004.
 
[32]  S. A. Billings and H. L. Wei, “A new class of wavelet networks for nonlinear system identification,” IEEE Trans. Neural Netw., vol. 16, no. 4, pp. 862-874, Jul. 2005.
 
[33]  J. Gonzalez-Nuevo, F. Argueso, M. Lopez-Caniego, L. Toffolatti, J. L. Sanz, P. Vielva, and D. Herranz, “The mexican hat wavelet family.application to point source detection in CMB maps,” Mon. Not. Roy. Astron. Soc., vol. 369, pp. 1603-1610, 2006.
 
[34]  Y. Oussar and G. Dreyfus, “Initialization by selection for wavelet network training,” Neurocomputing, vol. 34, no. 1, pp. 131-143, Sep. 2000.
 
[35]  R. Baron and B. Girau, “Parameterized normalization: Application to wavelet networks,” in Proc. IEEE Int. Conf. Neural Netw., May 1998, vol. 2, pp. 1433-1437.
 
[36]  Q. H. Zhang, “Using wavelet network in nonparametric estimation,” IEEE Trans. Neural Netw., vol. 8, no. 2, pp. 227-236, Mar. 1997.
 
[37]  M. Davanipoor, M. Zekri, and F. Sheikholeslam, “Fuzzy wavelet neural network with an accelerated hybrid learning algorithm,” IEEE Trans. Fuzzy Syst., vol. 20, no. 3, pp. 463-470, Jun. 2012.
 
[38]  H. Zhou, M. Chen, L. Zou, R. Gass, L. Ferris, L. Drogowski, and J. Rehg, “Spatially constrained segmentation of dermoscopy images,” in Proc. 5th IEEE Int. Symp. Biomed. Imag.: Nano Macro, May 2008, pp. 800-803.
 
[39]  F. Mokhtarian and S. Abbasi, “Shape similarity retrieval under affine transforms,” Pattern Recognit., vol. 35, no. 1, pp. 31-41, 2002.
 
[40]  R. S. Torres, A. X. Falc˜ao, and L. F. Costa, “A graph-based approach for multiscale shape analysis,” Pattern Recognit., vol. 37, no. 6, pp. 1163-1174, 2004.
 
[41]  A. Meijster and M. H. F. Wilkinson, “A comparison of algorithms for connected set openings and closings,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 4, pp. 484-494, Apr. 2002.