Advertisement

Tumor demarcation in mammographic images using vector quantization technique on entropy images

  • H. B. Kekre
  • Saylee M. Gharge
  • Tanuja K. Sarode
Conference paper

Abstract

Recent studies show that the interpretation of the mammograms by Radiologists gives high rates of false positive cases. Indeed the images provided by different patients have different dynamics of intensity and present a weak contrast. Moreover the size of the significant details can be very small. Several researchers have tried to develop computer aided diagnosis tools to help the radiologists in the interpretation of the mammograms for an accurate diagnosis. In order to perform a semi automated tracking of breast cancer, it is necessary to detect the presence or absence of lesions from the mammograms [1, 2].These lesions can be of various types: Nodular opacities, clear masses with lobed edges etc. They can be benign or malignant, according to their contour (sharp or blurred) – Stellar opacities (malignant tumors); micro calcifications: small calcified structures that appear as clear points on a mammogram [3, 4].

Keywords

Vector Quantization Training Vector Mammographic Image Source Symbol Color Image Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    E. E. Sterns, “Relation between clinical and mammographic diagnosis of breast problems and the cancer/ biopsy rate”. Can. J. Surg., vol. 39, no. 2, pp. 128-132, 1996Google Scholar
  2. 2.
    R. Highnam and M. Brady, Mammographic Image Analysis, Kluwer Academic Publishers, 1999. ISBN: 07923- 5620-9Google Scholar
  3. 3.
    Matthew A. Kupinski and Maryellen L. Giger, “Automated Seeded Lesion Segmentation”. IEEE Transaction on medical imaging, Vol. 17, No. 4, August 1998Google Scholar
  4. 4.
    Wirth, M.A. Stapinski, A., “Segmentation of the breast region in mammograms using active contours”, in Visual Communications and Image Processing, Switzerland, 2003, Vol. 5150, pp. 1995-2006Google Scholar
  5. 5.
    S. M. Lai, X. Li, and W. F. Bischof, “On techniques for detecting circumscribed masses in mammograms”. ZEEE Trans. Med. Zmag., vol. 8, no. 4, pp. 377-386, Dec. 1989CrossRefGoogle Scholar
  6. 6.
    W. Qian, L. P. Clarke, M. Kallergi, and R. A. Clark,. Treestructured nonlinear filters in digital mammography, IEEE Trans. Med. Imag., vol.13, no. 1, pp. 25-36, Mar. 1994CrossRefGoogle Scholar
  7. 7.
    D. Brzakovic, X. M. Luo, and P. Bzrakovic, “An approach to automated detection of tumors in mammography”, IEEE Trans. Med. Imag., vol. 9, no. 3, pp. 233-241, Sept. 1990CrossRefGoogle Scholar
  8. 8.
    F. F. Yin, M. L. Giger, K. Dol, C. E. Metz, R. A. Vyborny, and C. J. Schmidt, “Computerized detection of masses in digital mammograms: Analysis of bilateral subtraction images”, Med. Phys., vol. 18, no. 5, pp. 955-963, Sept. 1991CrossRefGoogle Scholar
  9. 9.
    T. K. Lau and W. F. Bischof, “Automated detection of breast tumors using the asymmetry approach”. Comput. Biomed. Res., vol. 24, pp.273-295, 1991CrossRefGoogle Scholar
  10. 10.
    W. P. Kegelmeyer Jr., J. M. Pruneda, P. D. Bourland, A. Hillis, M. W. Riggs, and M. L. Nipper., “Computer-aided mammographic screening for speculated lesions”, Radiol., vol. 191, no. 2, pp. 331-337, May 1994Google Scholar
  11. 11.
    D. Marr and E. Hildreth, “Theory of edge detection”, In Proceeding Royal Society, London., vol. 207, pp. 187-217, 1980CrossRefGoogle Scholar
  12. 12.
    J. Lunscher and M. P. Beddoes, “Optimal edge detector design: Parameter selection and noise effects”, IEEE Trans. Pattem Anal. Machine Intell., vol. 8, no. 2, pp. 154-176, Mar. 1986Google Scholar
  13. 13.
    Dr. H. B. Kekre, Saylee Gharge, “Segmentation of MRI Images using Probability and Entropy as Statistical parameters for Texture analysis”, Advances in Computational sciences and Technology(ACST),Volume 2,No.2, pp 219-230,2009Google Scholar
  14. 14.
    Dr. 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, 08-09 November, 2008Google Scholar
  15. 15.
    Dr. H. B. Kekre, Saylee Gharge, “Image Segmentation of MRI using Texture Features”,International Conference on Managing Next Generation Software Applications, School of Science and Humanities, Karunya University, Coimbatore, Tamilnadu, 05-06 December, 2008Google Scholar
  16. 16.
    Dr. H. B. Kekre, Saylee Gharge,. “Statistical Parameters like Probability and Entropy applied to SAR image segmentation”, International Journal of Engineering Research & Industry Applications (IJERIA), Vol.2,No.IV, pp.341-353Google Scholar
  17. 17.
    Dr. H. B. Kekre, Saylee Gharge, ”SAR Image Segmentation using co-occurrence matrix and slope magnitude”, ACM International Conference on Advances in Computing, Communication & Control (ICAC3-2009), pp.: 357-362, 23-24 Jan 2009, Fr. Conceicao Rodrigous College of Engg. Available on ACM portalGoogle Scholar
  18. 18.
    Dr. H. B. Kekre, Tanuja K. Sarode, Saylee Gharge,. “Detection and Demarcation of Tumor using Vector Quantization in MRI Images”. International Journal of Engineering Science and Technology(IJEST),Volume 2,No.2,pp:59-66,2009Google Scholar
  19. 19.
    C.E. Shannon, “A Mathematical Theory of Communication”, Bell System Technical Journal, vol.27, pp.379-423, 623- 656, July, October, 1948Google Scholar
  20. 20.
    H.B.Kekre, Tanuja K. Sarode, “New Fast Improved Clustering Algorithm for Codebook Generation for Vector Quantization”, International Conference on Engineering Technologies and Applications in Engineering, Technology and Sciences, Computer Science Department, Saurashtra University, Rajkot, Gujarat. (India), Amoghsiddhi Education Society, Sangli, Maharashtra (India), 13th. 14th January 2008Google Scholar
  21. 21.
    H. B. Kekre, Tanuja K. Sarode, “New Fast Improved Code-book Generation Algorithm for Color Images using Vector Quantization”, International Journal of Engineering and Technology, vol.1, No.1, pp.: 67-77, September 2008Google Scholar
  22. 22.
    H. B. Kekre, Tanuja K. Sarode, “Fast Codebook Generation Algorithm for Color Images using Vector Quantization”. International Journal of Computer Science and Information Technology, Vol. 1, No. 1, pp.: 7-12, Jan 2009Google Scholar
  23. 23.
    H. B. Kekre, Tanuja K. Sarode, “An Efficient Fast Algorithm to Generate Codebook for Vector Quantization”. First International Conference on Emerging Trends in Engineering and Technology, ICETET-2008, held at Raisoni College of Engineering, Nagpur, India, pp.: 62- 67, 16-18 July 2008. Avaliable at IEEE XploreGoogle Scholar
  24. 24.
    H. B. Kekre, Tanuja K. Sarode, “Fast Codebook Generation Algorithm for Color Images using Vector Quantization”. International Journal of Computer Science and Information Technology, Vol. 1, No. 1, pp.: 7-12, Jan 2009Google Scholar
  25. 25.
    H. B. Kekre, Tanuja K. Sarode, “Fast Codevector Search Algorithm for 3-D Vector Quantized Codebook”, WASET International Journal of cal Computer Information Science and Engineering (IJCISE), Volume 2, No. 4, pp.: 235-239, Fall 2008Google Scholar
  26. 26.
    H. B. Kekre, Tanuja K. Sarode, “Fast Codebook Search Algorithm for Vector Quantization using Sorting Technique”, ACM International Conference on Advances in Computing, Communication and Control (ICAC3- 2009), pp: 317-325, 23-24 Jan 2009, Fr. Conceicao Rodrigous College of Engg., Mumbai. Available on ACM portalGoogle Scholar
  27. 27.
    H. B. Kekre, Tanuja K. Sarode, “Speech Data Compression using Vector Quantization”, WASET International Journal of Computer and Information Science and Engineering (IJCISE), vol., No. 4, pp.: 251-254, Fall 2008. available: http://www.waset.org/ijcise
  28. 28.
    H. B. Kekre, Ms. Tanuja K. Sarode, Sudeep D. Thepade, “Image Retrieval using Color-Texture Features from DCT on VQ Codevectors obtained by Kekre.s Fast Codebook Generation”, ICGST-International Journal on Graphics, Vision and Image Processing (GVIP), Volume 9, Issue 5, pp.: 1-8, September 2009. Available online at http://www.icgst.com/ gvip/Volume9/Issue5/P1150921752. html
  29. 29.
    Chin-Chen Chang, Wen-Chuan Wu, “Fast Planar-Oriented Ripple Search Algorithm for Hyperspace VQ Codebook”, IEEE Transaction on image processing, vol 16, no. 6, pp.: 1538-1547, June 2007CrossRefGoogle Scholar
  30. 30.
    C. Garcia and G. Tziritas, “Face detection using quantized skin color regions merging and wavelet packet analysis,” IEEE Trans. Multimedia, vol. 1, no. 3, pp.: 264-277, Sep. 1999CrossRefGoogle Scholar
  31. 31.
    H. Y. M. Liao, D. Y. Chen, C. W. Su, and H. R. Tyan, “Realtime event detection and its applications to surveillance systems,. in Proc. IEEE Int. Symp. Circuits and Systems”, Kos, Greece, pp.: 509.512, May 2006Google Scholar
  32. 32.
    J. Zheng and M. Hu, “An anomaly intrusion detection system based on vector quantization”. IEICE Trans. Inf. Syst., vol. E89-D, no. 1, pp.: 201-210, Jan. 2006CrossRefGoogle Scholar
  33. 33.
    H. B. Kekre, Tanuja K. Sarode, Bhakti Raul, “Color Image Segmentation using Kekre.s Fast Codebook Generation Algorithm Based on Energy Ordering Concept”, ACM International Conference on Advances in Computing, Communication and Control (ICAC3-2009), pp.: 357-362, 23-24 Jan 2009, Fr. Conceicao Rodrigous College of Engg., Mumbai. Available on ACM portalGoogle Scholar
  34. 34.
    H. B. Kekre, Tanuja K. Sarode, Bhakti Raul, “Color Image Segmentation using Kekre.s Algorithm for Vector Quantization”, International Journal of Computer Science (IJCS), Vol. 3, No. 4, pp.: 287-292, Fall 2008. Available: http://www.waset.org/ijcs
  35. 35.
    H. B. Kekre, Tanuja K. Sarode, Bhakti Raul,. Color Image Segmentation using Vector Quantization Techniques Based on Energy Ordering Concept. International Journal of Computing Science and Communication Technologies (IJCSCT) Volume 1, Issue 2, pp: 164-171, January 2009Google Scholar
  36. 36.
    H. B. Kekre, Tanuja K. Sarode, Bhakti Raul,. Color Image Segmentation Using Vector Quantization Techniques., Advances in Engineering Science Sect. C (3), pp.: 35-42, July-September 2008Google Scholar
  37. 37.
    H. B. Kekre, Kamal Shah, Tanuja K. Sarode, Sudeep D. Thepade,. Performance Comparison of Vector Quantization Technique. KFCG with LBG, Existing Transforms and PCA for Face Recognition., International Journal of Information Retrieval (IJIR), Vol. 02, Issue 1, pp.: 64-71, 2009Google Scholar
  38. 38.
    Tou, J., and Gonzalez, Pattern Recognition Principles Addison-Wesley Publishing Company 1974Google Scholar

Copyright information

© Springer India Pvt. Ltd 2011

Authors and Affiliations

  • H. B. Kekre
    • 1
  • Saylee M. Gharge
    • 1
  • Tanuja K. Sarode
    • 2
  1. 1.NMIMS UniversityMumbaiIndia
  2. 2.Modern College of EngineeringMumbaiIndia

Personalised recommendations