CC BY-NC-ND 4.0 · Journal of Health and Allied Sciences NU 2012; 02(04): 50-53
DOI: 10.1055/s-0040-1703612
Short Communication

A NOVEL APPROACH FOR DIAGNOSING LIVER LESION IMAGES IN TELEMEDICINE MODE

Ulagamuthalvi V.
1   Sathyabama University, Chennai
,
Kulanthaivel G.
2   NITTTR, Chennai
,
Sridharan D.
3   Anna University, Chennai
› Author Affiliations

Abstract

In this article, a novel approach for diagnosing liver lesion using ultrasound image in telemedince mode is attempted. Liver cancer is a one of the neoplastic diseases. It has a high rate of mortality. The low quality of clinical ultrasound image limits the success of early detection and diagnosis based on the images. Filtering the spcekle noise to enhance the quality and segment the ROI from the ultrasound image are carried out. Wavelet-based texture descriptors are calculated and classification has been done using support vector machine. Telemedicine is the ability to provide interactive healthcare utilizing modern technology and telecommunications. In this telemedicne mode based diagnostic classification system, a client at remote location can submit the ultrasound B-scan liver image using the Internet and after analysis in the web server, the client at remote location will receive the diagnostic result.



Publication History

Article published online:
29 April 2020

© .

Thieme Medical and Scientific Publishers Private Ltd.
A-12, Second Floor, Sector -2, NOIDA -201301, India

 
  • References

  • 1 M. Sanaullah Chowdhury, Md. Humaun Kabir, Kazi Ashrafuzzaman, Kyung-Sup Kwak, 2009, ”Proceedings of International Journal of A Telecommunication Network Architecture for Telemedicine in Bangladesh and Its Applicability”, Digital Content Technology and its Applications, Vol: 3, No.: 3.
  • 2 C. Ruggiero F. Bagnoli, R. Sacile, M. Calabrese, G. Rescinito and F. Sardanelli, 1998, “Automatic Recognition of Malignant Lesions in Ultrasound Images by Artificial Neural Networks”, Proceedings of the th 20 IEEE Engineering in Medicine and Biology Society, Vol 20. No 2
  • 3 Haralick, K Shanmugam and I. H. Dinstein, 1973,'Texture Features for Image Classification,' IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, No.6,.
  • 4 Galloway, 'Texture Analysis Using Gray Level Run Lengths,' Computer Graphics and Image Processing, vol.4, pp. 172-179, 1975.
  • 5 Kara, Bayram, and Nurdal Watsuji. Using Wavelets for Texture Classification. IJCI Proceedings of International Conference on Signal Processing. ISSN 1304-2386, Columne:1, Number: 2. September 2003.
  • 6 Lindsay Semler, Lucia Dettori, Jacob Furst. Wavelet-based Texture Classification of Tissues in Computed Tomography, School of Computer Science, Telecommunications, and Information Systems DePaul University, Chicago, Illinois, 60604, USA
  • 7 S Wezka, C. R. Dryer, and A. Rosenfeld, 1976,“A comparative study of texture measures for terrain classification,” IEEE Trabs. Syst., Man, Cybern., vol. SMC-6, pp. 269-285, Apr,.
  • 8 Mayer., 1993, Wavelets: Algorithms and Applications, Philadelphia: SIAM
  • 9 M.V Wickerhauser., Adapted Wavelet Analysis from Theory of Software, IEEE Press.
  • 10 M Unser., 1994, Texture Classification and Segmentation Using Wavelet Frames, IEEE Trans. Image Processing, 4, 11, 1995, 1549-1560.
  • 11 Kulanthaivel G. and Ravindran G. 2003. ”Web Based Diagnostic aid for Kidney Lesions By Image Texture Parameters”, Biennial Conference of Indian Association for Medical Informatics, Chandigarh, p. 14.
  • 12 Wikipedia: www.en.wikipedia.org/wiki/Structured_SVM
  • 13 Murray H Loew, Rashidus Mia, and Zhenyu Guo,. 2000, “An Approach to Image Classification in Ultrasound” –IEEE Trans
  • 14 Ramachandra Lele, 2005, “Computers in Medicine – Progress in medical Informatics” Tata mcgraw-hill publishing company limited, New Delhi pp 504-528
  • 15 M Haralick 1,979., Statistical and structural approaches to texture, IEEE Proc., 67, 786-804
  • 16 Mallat S. and Zhong S. 1992, Characterization of signals from multiscale edges, IEEE Trans. Pattern Analysis and Machine Intelligence, 14,, 710-732
  • 17 Haralick, R, M., Shapiro, L. G., 1992, Computer and Robot Vision, Addison_wesley Publishing Co.
  • 18 Portal on forcasting with Artificial Neural Networks: www.neural_forecasting.com
  • 19 V.Ulagamuthalvi,D.Sridharan,(2012)”Development of Tele Medical Server for Remote Diagnostic Classification of Liver Lesion” Emerging Technology and Advanced Engineering, Vol.2, No:7,pp:59-62.