Alzheimer’s disease (AD) is a common form of senile dementia. Although the understanding of key steps underlying neurodegeneration in Alzheimer’s disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by patient. Conventional clinical decision making systems are more manual in nature and ultimate conclusion in terms of exact diagnosis is remote. 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 several imaging techniques used in clinical practice for the diagnosis of Alzheimer’s type pathology. There are lot of tests and neuroimaging 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), MRI Imaging and Optical Coherence Tomography (OCT). In this research we have proposed a new scheme based on Wavelet Networks (WN) for the feature extraction of MRI brain images for the early diagnosis of AD. The database of MRI images were obtained from Sree Gokulam Medical College and Research Foundation (SGMC&RF), Trivandrum, India.
| Published in | Bioprocess Engineering (Volume 1, Issue 2) |
| DOI | 10.11648/j.be.20170102.11 |
| Page(s) | 35-42 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Alzheimer Disease, Dementia, Wavelet Networks, Sombrero, MRI Image, Early Diagnosis
| [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,”A Psychometric Assessment Method for the Early Diagnosis of Alzheimer’s disease”, International Journal of Scientific & Engineering Research -IJSER (ISSN 2229-5518), Volume 8 Issue 3 –MARCH 2017. |
| [3] | 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, ISSN ONLINE 2359-1052, IJSART - Volume 3 Issue 3 –MARCH 2017: 366-371. |
| [4] | Sandeep C. S, Sukesh Kumar. A, “A software based on MMSE for screening the different stages of Alzheimer’s Disease (AD)”, ICSSP 2016, TKM Collge of Engineering. |
| [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] | AA, 2012. Alzheimer’s Facts and Figures. Alzheimer’s Association. |
| [7] | WAD, 2011. World Alzheimer’s Day on Wednesday. |
| [8] | ADI press release (http://www.alz.co.uk/media/nr100921.html) for “Alzheimer’s Disease International World Alzheimer Report 2010: The Global Economic Impact of Dementia,” Prof Anders Wimo, Karolinska Institutet, Stockholm, Sweden Prof Martin Prince, Institute of Psychiatry, King’s College London, UK. Published by Alzheimer’s Disease International (ADI) 21 September 2010. |
| [9] | 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, ISBN-10: 1416031219, pp: 1313-1317. |
| [10] | Harvey, R. A., P. C. Champe, B. D. Fisher, Lippincott’s Illustrated Reviews: Microbiology. 2nd Edn., Lippincott Williams and Wilkins, ISBN-10: 0781782155, pp: 432, 2006. |
| [11] | Cummings, J. L., H. V. Vinters, G. M. Cole and Z. S. Khachaturian, Alzheimer’s disease: etiologies, pathophysiology, cognitive reserve and treatment opportunities. Neurology. 51: 2-17. PMID: 9674758, 1998. |
| [12] | Yaari, R. and J. Corey-Bloom, Alzheimer’s disease: Pathology and pathophysiology. Semin Neurol. 27: 32-41, 2007. |
| [13] | Larson EB, Wang L, Bowen JD, et al. Exercise is associated with reduced risk for incident dementia among persons 65 years of age and older. Ann Intern Med; 144: 73-81, 2006. |
| [14] | Mayeux R. Epidemiology of neurodegeneration. Annu Rev Neurosci; 26: 81-104, 2003. |
| [15] | Harvey RJ, Skelton-Robinson M, Rossor MN. The prevalence and causes of dementia in people under the age of 65 years. J Neurol Neurosurg Psychiatry; 74: 1206-9, 2003. |
| [16] | Chu LW, Tam S, Wong RL, et al. Bioavailable testosterone predicts a lower risk of Alzheimer’s disease in older men. J Alzheimers Dis; 21: 1335-45, 2010. |
| [17] | 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. |
| [18] | 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. |
| [19] | 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. |
| [20] | Q. Zhang and A. Benveniste, “Wavelet networks,” IEEE Trans. Neural Netw., vol. 3, no. 6, pp. 889–898, Nov. 1992. |
| [21] | 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. |
| [22] | 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. |
| [23] | 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. |
| [24] | 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. |
| [25] | 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. |
| [26] | 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. |
| [27] | 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. |
| [28] | Y. Oussar and G. Dreyfus, “Initialization by selection for wavelet network training,” Neurocomputing, vol. 34, no. 1, pp. 131–143, Sep. 2000. |
| [29] | 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. |
| [30] | Q. H. Zhang, “Using wavelet network in nonparametric estimation,” IEEE Trans. Neural Netw., vol. 8, no. 2, pp. 227–236, Mar. 1997. |
| [31] | 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. |
| [32] | 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. |
| [33] | T. Lee, V. Ng, R. Gallagher, A. Coldman, and D. McLean, “DullRazor: A software approach to hair removal from images,” Comput. Biol. Med. Biol., vol. 27, no. 6, pp. 533–543, Nov. 1997. |
| [34] | Michael Freeman (2005). The Digital SLR Handbook. Ilex. ISBN 1-904705-36-7. |
| [35] | Korn, Theresa M.; Korn, Granino Arthur (2000). Mathematical Handbook for Scientists and Engineers: Definitions, Theorems, and Formulas for Reference and Review. Dover Publications. pp. 157–160. ISBN 0-486-41147-8. |
| [36] | Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346–359, 2008. |
| [37] | Jones, J. P.; Palmer, L. A. (1987). "An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex". J. Neurophysiol. 58 (6): 1233–1258. |
APA Style
Sandeep C. S., Sukesh Kumar A., K. Mahadevan, Manoj P. (2017). Feature Extraction of MRI Brain Images for the Early Detection of Alzheimer’s Disease. Bioprocess Engineering, 1(2), 35-42. https://doi.org/10.11648/j.be.20170102.11
ACS Style
Sandeep C. S.; Sukesh Kumar A.; K. Mahadevan; Manoj P. Feature Extraction of MRI Brain Images for the Early Detection of Alzheimer’s Disease. Bioprocess Eng. 2017, 1(2), 35-42. doi: 10.11648/j.be.20170102.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.be.20170102.11,
author = {Sandeep C. S. and Sukesh Kumar A. and K. Mahadevan and Manoj P.},
title = {Feature Extraction of MRI Brain Images for the Early Detection of Alzheimer’s Disease},
journal = {Bioprocess Engineering},
volume = {1},
number = {2},
pages = {35-42},
doi = {10.11648/j.be.20170102.11},
url = {https://doi.org/10.11648/j.be.20170102.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.be.20170102.11},
abstract = {Alzheimer’s disease (AD) is a common form of senile dementia. Although the understanding of key steps underlying neurodegeneration in Alzheimer’s disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by patient. Conventional clinical decision making systems are more manual in nature and ultimate conclusion in terms of exact diagnosis is remote. 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 several imaging techniques used in clinical practice for the diagnosis of Alzheimer’s type pathology. There are lot of tests and neuroimaging 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), MRI Imaging and Optical Coherence Tomography (OCT). In this research we have proposed a new scheme based on Wavelet Networks (WN) for the feature extraction of MRI brain images for the early diagnosis of AD. The database of MRI images were obtained from Sree Gokulam Medical College and Research Foundation (SGMC&RF), Trivandrum, India.},
year = {2017}
}
TY - JOUR T1 - Feature Extraction of MRI Brain Images for the Early Detection of Alzheimer’s Disease AU - Sandeep C. S. AU - Sukesh Kumar A. AU - K. Mahadevan AU - Manoj P. Y1 - 2017/06/26 PY - 2017 N1 - https://doi.org/10.11648/j.be.20170102.11 DO - 10.11648/j.be.20170102.11 T2 - Bioprocess Engineering JF - Bioprocess Engineering JO - Bioprocess Engineering SP - 35 EP - 42 PB - Science Publishing Group SN - 2578-8701 UR - https://doi.org/10.11648/j.be.20170102.11 AB - Alzheimer’s disease (AD) is a common form of senile dementia. Although the understanding of key steps underlying neurodegeneration in Alzheimer’s disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by patient. Conventional clinical decision making systems are more manual in nature and ultimate conclusion in terms of exact diagnosis is remote. 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 several imaging techniques used in clinical practice for the diagnosis of Alzheimer’s type pathology. There are lot of tests and neuroimaging 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), MRI Imaging and Optical Coherence Tomography (OCT). In this research we have proposed a new scheme based on Wavelet Networks (WN) for the feature extraction of MRI brain images for the early diagnosis of AD. The database of MRI images were obtained from Sree Gokulam Medical College and Research Foundation (SGMC&RF), Trivandrum, India. VL - 1 IS - 2 ER -
Information
Email: