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Reseach Article

Early Diagnosis of Parkinson’s Disease based on Transcranial Sonography

by Valanarasi Antony Santiagu Vaz
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 5
Year of Publication: 2013
Authors: Valanarasi Antony Santiagu Vaz
10.5120/11839-7567

Valanarasi Antony Santiagu Vaz . Early Diagnosis of Parkinson’s Disease based on Transcranial Sonography. International Journal of Computer Applications. 69, 5 ( May 2013), 22-28. DOI=10.5120/11839-7567

@article{ 10.5120/11839-7567,
author = { Valanarasi Antony Santiagu Vaz },
title = { Early Diagnosis of Parkinson’s Disease based on Transcranial Sonography },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 5 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 22-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number5/11839-7567/ },
doi = { 10.5120/11839-7567 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:29:26.355787+05:30
%A Valanarasi Antony Santiagu Vaz
%T Early Diagnosis of Parkinson’s Disease based on Transcranial Sonography
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 5
%P 22-28
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Transcranial Sonography (TCS) plays a very significant role in the early analysis of Parkinson's disease (PD). The TCS taken in the mesencephelon region shows a discrete pattern with increase in size (hyper echogenecity) of substantia nigra in about 90% of PD patients. Generally this hyperechogenic pattern is segmented physically which can be used as PD indicator for early diagnosis. This paper proposes a novel procedure using GLCM and Multi Layer Perceptron Neural Network for the early PD risk assessment. The features are obtained by a assortment of Gabor filters, and the concert of these features is evaluated by feature selection method. At an earlier stage speckle noise is removed using spatially adaptive wiener filter. This method is well applicable with neural network toolbox in MATLAB.

References
  1. Berg D. , Godau J. , Walter U. , " Transcranial sonography in movement disorders. " The Lancet Neurology. 2008. Vol. 7 (11). P. 1044–1055.
  2. K. Thangavel, R. Manavalan, Laurence Aroquiaraj, "Removal of Speckle Noise from Ultrasound Medical Image based on Special Filters: Comarative study", ICGST-GVIP Journal, Vol. 9, Issue:3, pp. 25-32, June 2009.
  3. Khaled Z. AbdElmoniem, Yasser M. Kadah and AbouBakr M. Youssef, "Real Time Adaptive Ultrasound Speckle Reduction and Coherence Enhancement", 078032977/00/$10© 2000 IEEE, pp. 172-175.
  4. Kier C. , Seidel G. , Bregemann N. , Hagenah J. , Klein C. , Aach T. , Mertins A. , "Transcranial sonography as early indicator for genetic parkinson's disease". 4th European Conference of IFMBE. 2009. P. 456-459.
  5. Chen L. , Hagenah J. , and Mertins A, ". Texture Analysis Using Gabor filter Based on Transcranial Sonography Image". Proceedings of 2011 Bvm bildverarbeitung fur die medizin. 2011. P. 249-253.
  6. Chen L. , Seidel G. , Mertins A. , " Multiple Feature Extraction for E]rly Parkinson Risk Assessment Based on Transcranial Sonography Image" . Proceedings of 2010 IEEE 17th International Conference on Image Processing. 2010. P. 2277-2280.
  7. Szczypinski P. , Strzelecki M. , Materka A. , Klepaczko A. "MaZda-A software package for image texture analysis", Computer Methods and Programs in Biomedicine. 2009. Vol. 94(1). P. 66-76.
  8. Szczypinski P. , Strzelecki M. , Materka A. , " MaZda - a Software for Texture Analysis", Proc. of ISITC 2007, November 23-23, Republic of Korea. 2007. P. 245-249.
  9. Manjunath B. S. , Ma W. Y. , " Texture features for browsing and retrieval of image data" . IEEE Transactions PAMI. 1996. Vol. 18(8). P. 837 – 842
  10. A. Sakalauskas1, A. Lukoševi?ius, K. Lau?kait?, " Texture analysis of transcranial sonographic images for Parkinson disease ISSN 1392-2114 ULTRAGARSAS (ULTRASOUND), Vol. 66, No. 3, 2011.
  11. Walter U, Behnke S, Eyding J, et al. , " Transcranial brain parenchyma sonography in movement disorders: State of the art". Ultrasound in Medicine & Biology. 2007;33(1):15–25.
  12. http://www. ninds. nih. gov/disorders/parkinsonsdisease /parkinsons_disease. html.
  13. V. B. Rao, C++ neural networks and fuzzy logic: MTBooks, IDG Books Worldwide, Inc. , 1995.
  14. D. Anderson and G. McNeill, "Artificial neural network technology," Rome Laboratory, New York1992.
  15. J. Tebelskis, "Speech recognition using neural networks," PhD, Carnegie Mellon University, Pittsburgh, Pennysylvania, 1995.
  16. L. Pretchelt, "Early stopping - but when?," Neural Networks: Trick of the Trade, vol. 1524, pp. 55-69, 1996.
  17. Weisstein, Eric W. "Levenberg-Marquardt Method", http://mathworld. wolfram. com/Levenberg- arquardt Method. html
  18. M. T. Hagan and M. B. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE Trans. on Neural Networks, vol. 5, pp. 989 - 993, 1994.
Index Terms

Computer Science
Information Sciences

Keywords

GLCM Mesencephalon Hyperechogenic Spatially adaptive wiener filter Substantia nigra TCS