CFP last date
20 December 2024
Reseach Article

Article:Tumor Demarcation by using Local Thresholding on Selected Parameters obtained from Co-occurrence Matrix of Ultrasound Image of Breast

by Dr. H. B. Kekre, Pravin Shrinath
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 7
Year of Publication: 2011
Authors: Dr. H. B. Kekre, Pravin Shrinath
10.5120/3914-5510

Dr. H. B. Kekre, Pravin Shrinath . Article:Tumor Demarcation by using Local Thresholding on Selected Parameters obtained from Co-occurrence Matrix of Ultrasound Image of Breast. International Journal of Computer Applications. 32, 7 ( October 2011), 9-15. DOI=10.5120/3914-5510

@article{ 10.5120/3914-5510,
author = { Dr. H. B. Kekre, Pravin Shrinath },
title = { Article:Tumor Demarcation by using Local Thresholding on Selected Parameters obtained from Co-occurrence Matrix of Ultrasound Image of Breast },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 7 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 9-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number7/3914-5510/ },
doi = { 10.5120/3914-5510 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:33.009868+05:30
%A Dr. H. B. Kekre
%A Pravin Shrinath
%T Article:Tumor Demarcation by using Local Thresholding on Selected Parameters obtained from Co-occurrence Matrix of Ultrasound Image of Breast
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 7
%P 9-15
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Ultrasound imaging (US) is the most widely used and important imaging modality in medical domain. Due to certain artifact such as speckle, segmentation of US image has not remained a trivial task. Two stages segmentation process has been used in this paper to detect the solid mass (cancer) in breast US image. GLCM based texture feature image generation followed by local adaptive thresholding. In first, Correlation, Variance, Sum variance and Sum average texture features for all angular relationships has been implemented on original image to obtain the feature images. In Second, adaptive local thresholdinig algorithm is applied recursively by dividing the feature image into nine sub-images and compared with the result of Otsu’s global thresholding technique. Results of our algorithm are better.

References
  1. Sickles EA., “Breast imaging: from 1965 to the present”, Radiology, 215(1): pp 1-16, April 2000.
  2. Sehqal CM, Weinstein SP,Arqer PH, Conant EF, “ A review of breast ultrasound”, J Mammary Gland Bio Neoplasia, 11 (2), pp 113-123, April 2006
  3. J. Alison Noble, Djamal Boukerroui, “Ultrasound Image Segmentation: A Survey”, IEEE Transactions on Medical Imaging, Vol. 25, No. 8, pp 987-1010, Aug 2006
  4. Christos P. Loizou, Constantinos S. Pattichis, Christodoulos I. Christodoulou, Robert S. H. Istepanian, Marios Pantziaris, and Andrew Nicolaides “Comparative Evaluation of Despeckle Filtering In Ultrasound Imaging of the Carotid Artery” IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 52, no.10, pp 46-50, October 2005
  5. Steven W. Zucker and Demetri Terzopoulos, “ Finding Structure in Co-occurrence Matrices for Texture Analysis” Computer Graphics and Image Processing, Vol 12, No 3, pp 286-308, March 1980.
  6. ZHU Chang-ming, GU Guo-chang, LIU Hai-bo, Shen Jing, YU Hualong, “ Segmentation of Ultrasound Image Based on Texture Feature and Graph Cut”, IEEE International Conference on Computer Science and Software Engineering, Vol 1, pp 795-798, Dec 2008.
  7. Jun Xie, Yifeng Jiang, and Hung-tat Tsui, “Segmentation of Kidney From Ultrasound Images Based on Texture and Shape Priors”, IEEE Transactions on Medical Imaging, Vol.24, No.1, pp 45-57, January 2005.
  8. Qiuxia Chen, Qi Liu, “Textural Feature Analysis for Ultrasound Breast Tumor Images”, 4th IEEE International conference on Bioinformatics and Biomedical Engineering, Chengdu, china, pp 1-4, June 2010.
  9. Vibhakar Shrimali, R.S.Ananad, Vinod Kumar, “Current trends in Segmentation of medical ultrasound B-mode Images: A review”, IETE technical review, Vol.26, Issue 1, pp 8-17, Jan 2009.
  10. R.M.Haralick, K. Shanmugam, I. Dinstein, “Texture Feature for Image Classification”, IEEE Transaction on System, Man and Cybernetics, Vol. SMC-3,No.6, pp.610-621, November 1973.
  11. H. B. Kekre , Saylee Gharge , “Selection of Window Size for Image Segmentation using Texture Features,” International Conference on Advanced Computing & Communication Technologies (ICACCT-2008) Asia Pacific Institute of Information Technology SD India, Panipat, November, 2008
  12. Antonio Fern´andez-Caballero, Juan L. Mateo “Methodological Approach to Reducing Speckle Noise in Ultrasound Images”, in the proceeding of IEEE International Conference on BioMedical Engineering and Informatics, Vol 2, pp 147-154, 2008.
  13. R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, Addison-Wesley Publishing Company, 2002.
  14. N.Otsu, “A threshold selection method from gray-level histogram”, IEEE Transaction on Syst., Man, Cybern., Vol. SMC-9,No 1, pp 62-66, 1979.
  15. J.R.Parker, “Gray level thresholding in badly illuminated images”, IEEE Trans. On Pattern Analysis and Machine Intell., Vol. 13, No.8, pp.813-819, Aug 1991.
  16. Satya Swaroop Pradhan, Dipti Patra, Pradipta Kumar Nanda, “Adaptive Thresholding Based Image Segmentation with Uneven Lighting Condition”, 2008 IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA, pp 1-6, December 2008
  17. F. Yan, H. Zhang, and C.R. Kube, "A multistage adaptive thresholding method", presented at Pattern Recognition Letters, pp.1183-1191, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Ultrasound Image Gray Level Co-occurrence Matrix Adaptive Thresholding Feature Images