International Journal of
Computer Sciences and Engineering

Scholarly Peer-Reviewed, and Fully Refereed Scientific Research Journal
An Effective Method for early Diagnosis of Alzheimer Disease using Angular Radial Transform and Orthogonal Fourier Mellin Moments
An Effective Method for early Diagnosis of Alzheimer Disease using Angular Radial Transform and Orthogonal Fourier Mellin Moments
R. Upneja1 , A. Prashar2
1 Dept. of Mathematics, Sri Guru Granth Sahib World University, Fatehgarh Sahib, India.
2 Dept. of Mathematics, Trinity College, Jalandhar, India.
Correspondence should be addressed to:

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-10 , Page no. 1-9, Oct-2017


Online published on Oct 30, 2017

Copyright © R. Upneja, A. Prashar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
View this paper at   Google Scholar | DPI Digital Library
  XML View PDF Download  

IEEE Style Citation: R. Upneja, A. Prashar, “An Effective Method for early Diagnosis of Alzheimer Disease using Angular Radial Transform and Orthogonal Fourier Mellin Moments”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.1-9, 2017.

MLA Style Citation: R. Upneja, A. Prashar "An Effective Method for early Diagnosis of Alzheimer Disease using Angular Radial Transform and Orthogonal Fourier Mellin Moments." International Journal of Computer Sciences and Engineering 5.10 (2017): 1-9.

APA Style Citation: R. Upneja, A. Prashar, (2017). An Effective Method for early Diagnosis of Alzheimer Disease using Angular Radial Transform and Orthogonal Fourier Mellin Moments. International Journal of Computer Sciences and Engineering, 5(10), 1-9.
Downloads (121)     Full view (113)
Abstract :
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder dementia. The main challenges for medical investigators have been the early diagnosis of patients with AD because an early diagnosis can provide greater opportunities for patients to be eligible for more clinical trials. The transitional state between healthy control (HC) and AD with mild memory problems is Mild cognitive impairment (MCI). A reliable diagnosis of MCI can be very effective for early diagnosis of AD. In this study, a fast and accurate method based on rotation invariant descriptors is proposed and moments are used to distinguish the patients with AD and MCI from normal participants (HC) using structural Magnetic Resonance Images (MRI). The rotation invariant descriptors are among the best region based shape descriptors which are used in many medical image processing applications. The angular radial transform (ART) is one such rotation invariant descriptors. This descriptor has two essential characteristics as compared to moment based descriptors, viz., it has low computation cost and provides a large number of numerically stable features. However, its kernel consists of the sinusoidal functions which still needs high computation time. In this paper, we developed fast and effective method to compute the radial & angular sinusoidal functions using 8-way symmetry and also used fast & recursive method to extract the features from MRI images using OFMMs. These methods are used not only for binary images but for gray level images also. The proposed method is not only fast but also more reliable and numerically stable.
Key-Words / Index Term :
Alzheimer, Early Diagnosis, Rotation Invariant Descriptors, Angular Radial Transform, Mild cognitive impairment, Healthy control, Orthogonal Fourier Mellin Moments, Zernike Moments
References :
[1] R.S. Wilson, E. Segawa, P.A. Boyle, S.E. Anagnos, L.P. Hizel, D.A. Bennett, “The natural history of cognitive decline in Alzheimer’s diseas”, Psychol Aging, Vol. 27, Issue 4, pp. 1008-1017, 2012.
[2] W.W. Barker, C.A. Luis, A. Kashuba, M. Luis, D.G. Harwood, D. Loewenstein, C. Waters, P. Jimison, E. Shepherd, S. Sevush, N. Graff-Radford, D. Newland, M. Todd, B. Miller, M. Gold, K. Heilman, L. Doty, G. Pearl, D. Dickson, R. Duara, “Relative frequencies of Alzheimer’s disease, Lewy body, vascular and frontotemporal dementia, and hippocampal sclerosis in the State of Florida Brain Bank”, Alzheimer Dis Assoc. Disorder, Vol. 16, Issue 4, pp. 203-212, 2002.
[3] M.M. Lopez, J. Ramierz, J.M. Gorriz, I. Alvarez, D. Salas-Gonzalez, F. Sequovia, R. Chaves, “SVM-based CAD system for early detection of the Alzheimer’s disease using kernel PCA and LD”, Neuroscience, Vol. 464, Issue 3, pp. 233-238, 2009.
[4] R. Brookmeyer, S. Gray, C. Kaeas, “Projections of Alzheimer’s disease in the 657 United States and the public health impact of delaying disease onset”, Am J Public Health, Vol. 88, Issue 9, pp. 1337–1342, 1998.
[5] N.C. Berchtold, C.W. Cotman, “Evolution in the conceptualization of dementia and Alzheimer’s disease: Greco-Roman period to the 1960s”, Neurobiology of Aging, Vol. 19, Issue 3, pp. 173–189, 1998.
[6] S.C. Johnson, T.W. Schmitz, C.H. Moritz, M.E. Meyerand, H.A. Rowley, A.L. Alexander, K.W. Hansen, C.E. Gleason, C.M. Carlsson, M.L. Ries, S. Asthana, K. Chen, G.E. Alexander, “Activation of brain regions vulnerable to Alzheimer’s disease: the effect of mild cognitive impairment”, Neurobiology of Aging, Vol. 27, Issue 11, pp. 1604–1612, 2006.
[7] P.M. Thompson, L.G. Apostolova, “Computational anatomical methods as applied to ageing and dementia”, Br J Radiol, Vol. 80, Issue 2, pp. S78–S91, 2007.
[8] J.L. Whitwell, S. Przybelski, S.D. Weigand, D.S. Knopman, B.F. Boeve, R.C. Petersen, C.R. Jack Jr., “3D maps from multiple MRI illustrate changing atrophy patterns as subject’s progress from mild cognitive impairment to Alzheimer’s disease”, Brain, Vol. 130, Issue 7, pp. 1777–1786, 2007.
[9] M. Grundman, R.C. Petersen, S.H. Ferris, “Mild cognitive impairment can be distinguished from Alzheimer’s disease and normal aging for clinical trials”, Arch Neurol., Vol. 61, Issue 1, pp. 59–66, 2004.
[10] J. Bischkopf, A. Busse, M.C. Angermeyer, “Mild cognitive impairment-a review of prevalence, incidence and outcome according to current approaches”, Acta Psychiatr Scand., Vol. 106, Issue 6, pp. 403–414, 2002.
[11] T. Erkinjuntti, R. Sulkava, J. Palo, L. Ketonen, “White matter low attenuation on CT in Alzheimer’s disease”, Arch Gerontol Geriatr, Vol. 8, Issue 1, pp. 95–104, 1989.
[12] H. Toyama, D. Ye, M. Ichise, “ PET imaging of brain with the b-amyloid probe,[11C] 6-OH-BTA-1, in a transgenic mouse model of Alzheimer’s disease”, Eur J Nucl Med Mol Imaging, Vol. 32, Issue 5, pp. 593–600, 2005.
[13] C. Davatzikos, Y. Fan, X. Wu, Dg. Shen, S.M. Resnick, “Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging”, Neurobiology of Aging, Vol. 29, Issue 4, pp. 514–523, 2008.
[14] R. Chaves, J. Ramirez, J.M. Gorriz, M. Lopez, D. Salas-Gonzalez, I. Alvarez, F. Segovia, “SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting”, Neurosci. Lett., Vol. 461, Issue 3, pp. 293–297, 2009.
[15] R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehericy, M.O. Habert, M. Chupin, H. Benali, O. Colliot, “Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database”, Neuro Image, Vol. 56, Issue 2, pp.766–781, 2011.
[16] B. Magnin, L. Mesrob, S. Kinkingnehun, M. Pelegrini-Issac, O. Colliot, M. Sarazin,B. Dubois, S. Lehericy, H. Benali, “Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI”, Neuroradiology, Vol. 51, Issue 2, pp. 73–83, 2009.
[17] S. Kloppel, C.M. Stonnington, C. Chu, B. Draganski, R.I. Schaijl, J.D. Rohrer, N.C. Fox, C.R. Jack, J. Jr, Ashburner, R.S Frackowiak, “Automatic classification of MR scans in Alzheimer’s disease”, Brain, Vol. 131, Issue 3, pp. 681–689, 2008.
[18] D. Shen, C.Y. Wee, D. Zhang, L. Zhou, P.T. Yap, “Machine Learning Techniques for AD/MCI Diagnosis and Prognosis”, Machine Learning in Healthcare Springer, Vol. 56, pp. 147–179, 2012.
[19] W. Yaping, “Kernel-based multi-task joint sparse classification for Alzheimer’s disease” ISBI, 10th International Symposium on Biomedical Imaging, IEEE, 2013.
[20] S.T. Yang, J.D. Lee, T.C. Chang, C.H. Huang, J.J. Wang, W.C. Hsu, H.L. Chan, Y.Y. Wai, K.Y. Li, “Discrimination between Alzheimer’s disease and Mild Cognitive Impairment Using SOM and PSO-SVM” Computational and mathematical methods in medicine, 2013.
[21] J. Escudero, J.P. Zalicek, E. Ifeachor, “Machine Learning classification of MRIfeatures of Alzheimer’s disease and mild cognitive impairment subjects to reduce the sample size in clinical trials”, Engineering in Medicine and Biology Society, EMBC, Annual International Conference of the IEEE, 2011.
[22] O. Colliot, G. Chetelat, M. Chupin, B. Desgranges, B. Maqnin, H. Benali, B. Dubois, L. Garnero, F. Eustache, S. Lehericy, “ Discrimination between Alzheimer Disease, Mild Cognitive Impairment, and Normal Aging by Using Automated Segmentation of the Hippocampus 1”, Radiology, Vol. 248 671, Issue 1, pp. 194–201, 2008.
[23] C. Vinitha, M. Azath, “Study on Image Authentication Techniques”, International Journal of Computer Sciences and Engineering, Vol. 2, Issue 12, pp. 87-89, 2014.
[24] H.T. Gorji, J. Haddadnia, “A novel method for early diagnosis of Alzheimer`s disease based on pseudo Zernike moment from structural MRI”, Neuroscience, Vol. 305, pp. 361-371, 2015.
[25] W. Kim, Y. Kim., “A new region-based shape descriptor: the ART (Angular Radial Transform) descriptor”, ISO/IEC JTC1/SC29/WG11/M5472, 2001.
[26] M. Bober, “MPEG-7 visual shape descriptors”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, Issue 6, pp. 716–719, 2001.
[27] A. Amanatiadis, V.G. Kaburlasos, A. Gasteratos, S.E.Papadakis, “Evaluation of shape descriptors for shape-based image retrieval. Image Processing”, IET, Vol. 5, Issue 5, pp. 493-499, 2011.
[28] S.K. Hwang, W.Y. Kim, “Fast and efficient method for computing ART”, IEEE Transactions Image Processing, Vol. 15, Issue 1, pp. 112-117, 2006.
[29] L. Kotoules, I. Andreadis, “An efficient technique for the computation of ART”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 18, Issue 5, pp. 682-686, 2008.
[30] Y. Sheng, L. Shen, “Orthogonal Fourier-Mellin moments for invariant pattern recognition”, J. Opt. Soc. Am., Vol. 11, Issue 6, pp. 1748-1757, 1994.
[31] E. Walia, C. Singh, A. Goyal, “On the fast computation of orthogonal Fourier- Mellin moments with improved numerical stability”, J Real- Time Image Proc., Vol. 7, pp. 247-256, 2012.
[32] S.K. Hwang, W.Y. Kim, “A novel approach to the fast computation of Zernike moments”, Pattern Recognition, Vol. 39, pp. 2065–2076, 2006.
[33] C. Singh, R. Upneja, “Fast and accurate method for high order Zernike moments computation”, Applied Mathematics and Computation, Vol. 218, pp. 7759-7773, 2012.
[34] C. Singh, A. Kaur, “Fast computation of Polar Harmonic Transforms”, Journal of Real-Time image processing, Vol. 10, Issue 1, pp. 59-66, 2015.
[35] G.A. Papakostas, Y.S. Boutalis, D.A. Karras, B.G. Mertzio, “Fast numerically stable computation of orthogonal Fourier- Mellin moments”, IET Computer. Vis., Vol. 1, Issue 1, pp. 11-16, 2007.