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Vector Quantization for Tumor Demarcation of Mammograms

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Information Processing and Management (BAIP 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 70))

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Abstract

Segmenting mammographic images into homogeneous texture regions representing disparate tissue types is often a useful preprocessing step in the computer-assisted detection of breast cancer. Hence new algorithm to detect cancer in mammogram breast cancer images is proposed. In this paper we proposed segmentation using vector quantization technique. Here Linde Buzo Gray (LBG) for segmentation of mammographic images is used. Initially a codebook (CB) of size 128 was generated for mammographic images. These code vectors were further clustered in 8 clusters using same algorithm. These 8 images were displayed as a result. The codebook of size 128 clustered to 16 code vectors, codebook of size 128 clustered to 8 code-vectors using LBG algorithm is compared with watershed algorithm. The proposed approach does not lead to over segmentation or under segmentation with less complexity with more accuracy.

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References

  1. Kegelmeyer, W.P.: Computer detection of stellate lesions in mammograms. In: Proc. SPIE Biomed. Image Processing, vol. 1660, pp. 446–454 (1992)

    Google Scholar 

  2. Qian, W., Clarke, L.P., Kallergi, M., Li, H., Velthuizen, R., Clark, R.A., Silbiger, M.L.: Tree-structured nonlinear filter and wavelet transform for microcalcification segmentation in mammography. In: SPIE Biomed. Image Processing and Biomed. Visual., vol. 1905, pp. 509–520 (1993)

    Google Scholar 

  3. Zhao, D.: Rule-based morphological feature extraction of microcalcifications in mammograms. In: SPIE Med. Imag., vol. 1095, pp. 702–715 (1993)

    Google Scholar 

  4. Lo, S.C., Chan, H.P., Lin, J.S., Li, H., Freedman, M.T., Mun, S.K.: Artificial convolution neural network for medical image pattern recognition. Neural Networks 8(7/8), 1201–1214 (1995)

    Article  Google Scholar 

  5. Karssemeijer, N.: Recognition of clustered microcalcifications using a random field model. In: SPIE Med. Imag., vol. 1905, pp. 776–786 (1993)

    Google Scholar 

  6. Lefebvre, F., Benali, H., Gilles, R., Kahn, E., Paola, R.D.: A fractal approach to the segmentation of microcalcification in digital mammograms. Med. Phys. 22(4), 381–390 (1995)

    Article  Google Scholar 

  7. Yoshida, H., Doi, K., Nishikawa, R.M.: Automated detection of clustered microcalcifications in digital mammograms using wavelet transform techniques. In: SPIE Image Processing, vol. 2167, pp. 868–886 (1994)

    Google Scholar 

  8. Laine, F., Schuler, S., Fan, J., Huda, W.: Mammographic feature enhancement by multiscale analysis. IEEE Trans. Med. Imag. 13(4), 725–740 (1994)

    Article  Google Scholar 

  9. Tou, J., Gonzalez: Pattern Recognition Principles. Addison-Wesley Publishing Company, Reading (1974)

    MATH  Google Scholar 

  10. Kekre, H.B., Gharge, S.: Selection of Window Size for Image Segmentation using Texture Features. In: Proceedings of International Conference on Advanced Computing & Communication Technologies (ICACCT 2008) Asia Pacific Institute of Information Technology SD India, Panipat, November 08-09 (2008)

    Google Scholar 

  11. Kekre, H.B., Gharge, S.: Image Segmentation of MRI using Texture Features. In: Proceedings of International Conference on Managing Next Generation Software Applications, School of Science and Humanities, Karunya University, Coimbatore, Tamilnadu, December 05-06 (2008)

    Google Scholar 

  12. Kekre, H.B., Gharge, S.: Statistical Parameters like Probability and Entropy applied to SAR image segmentation. International Journal of Engineering Research & Industry Applications (IJERIA) 2(IV), 341–353

    Google Scholar 

  13. Kekre, H.B., Gharge, S.: SAR Image Segmentation using co-occurrence matrix and slope magnitude. In: ACM International Conference on Advances in Computing, Communication and Control (ICAC3 2009), Fr. Conceicao Rodrigous College of Engg., Mumbai, January 23-24, pp. 357–362 (2009) (Available on ACM portal)

    Google Scholar 

  14. Haralick, R.M.: IEEE Proceedings of Statistical and Structural Approaches to Texture 67(5) (May 1979)

    Google Scholar 

  15. Shafarenko, L., Petrou, M.: Automatic Watershed Segmentation of Randomly Textured Color Images. IEEE Transactions on Image Processing 6(11), 1530–1544 (1997)

    Article  Google Scholar 

  16. Alhadidi, B., Mohammad, H., et al.: Mammogram Breast Cancer Edge Detection Using Image Processing Function. Information Technology Journal 6(2), 217–221 (2007)

    Article  Google Scholar 

  17. Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Transactions on Communication COM-28, 85–94 (1980)

    Google Scholar 

  18. Gray, R.M.: Vector quantization. IEEE ASSP Magazine 1, 4–29 (1984)

    Article  Google Scholar 

  19. Kekre, H.B., Sarode, T.K.: New Fast Improved Clustering Algorithm for Codebook Generation for Vector Quantization. In: 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, January 13-14 (2008)

    Google Scholar 

  20. Kekre, H.B., Sarode, T.K.: New Fast Improved Codebook Generation Algorithm for Color Images using Vector Quantization. International Journal of Engineering and Technology 1(1), 67–77 (2008)

    Google Scholar 

  21. Kekre, H.B., Sarode, T.K.: Fast Codebook Generation Algorithm for Color Images using Vector Quantization. International Journal of Computer Science and Information Technology 1(1), 7–12 (2009)

    Google Scholar 

  22. Kekre, H.B., Sarode, T.K.: An Efficient Fast Algorithm to Generate Codebook for Vector Quantization. In: First International Conference on Emerging Trends in Engineering and Technology, ICETET 2008, Raisoni College of Engineering, Nagpur, India, July 16-18, pp. 62–67 (2008), IEEE Xplore

    Google Scholar 

  23. Kekre, H.B., Sarode, T.K.: Fast Codebook Generation Algorithm for Color Images using Vector Quantization. International Journal of Computer Science and Information Technology 1(1), 7–12 (2009)

    Google Scholar 

  24. Kekre, H.B., Sarode, T.K.: Fast Codevector Search Algorithm for 3-D Vector Quantized Codebook. WASET International Journal of cal Computer Information Science and Engineering (IJCISE) 2(4), 235–239 (Fall 2008), http://www.waset.org/ijcise

    Google Scholar 

  25. Kekre, H.B., Sarode, T.K.: Fast Codebook Search Algorithm for Vector Quantization using Sorting Technique. In: ACM International Conference on Advances in Computing, Communication and Control (ICAC3 2009), Fr. Conceicao Rodrigous College of Engg., Mumbai, January 23-24, pp. 317–325 (2009) (Available on ACM portal)

    Google Scholar 

  26. Kekre, H.B., Sarode, T.K., Raul, B.: Color Image Segmentation using Kekre’s Fast Codebook Generation Algorithm Based on Energy Ordering Concept. In: ACM International Conference on Advances in Computing, Communication and Control (ICAC3 2009), Fr. Conceicao Rodrigous College of Engg., Mumbai, January 23-24, pp. 357–362 (2009) (Available on ACM portal)

    Google Scholar 

  27. Kekre, H.B., Sarode, T.K., Raul, B.: Color Image Segmentation using Kekre’s Algorithm for Vector Quantization. International Journal of Computer Science (IJCS) 3(4), 287–292 (Fall 2008), http://www.waset.org/ijcs

    Google Scholar 

  28. Kekre, H.B., Sarode, T.K., Raul, B.: Color Image Segmentation using Vector Quantization Techniques Based on Energy Ordering Concept. International Journal of Computing Science and Communication Technologies (IJCSCT) 1(2), 164–171 (2009)

    Google Scholar 

  29. Kekre, H.B., Sarode, T.K., Raul, B.: Color Image Segmentation Using Vector Quantization Techniques. Advances in Engineering Science Sect. C (3), 35–42 (2008)

    Google Scholar 

  30. Kekre, H.B., Sarode, T.K.: Speech Data Compression using Vector Quantization. WASET International Journal of Computer and Information Science and Engineering (IJCISE) 2(4), 251–254 (Fall 2008), http://www.waset.org/ijcise

    Google Scholar 

  31. Kekre, H.B., Sarode, T.K., Thepade, S.D.: 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) 9(5), 1–8 (2009), http://www.icgst.com/gvip/Volume9/Issue5/P1150921752.html

    Google Scholar 

  32. Kekre, H.B., Sarode, T.K., Thepade, S.D.: Color-Texture Feature based Image Retrieval using DCT applied on Kekre’s Median Codebook. International Journal on Imaging (IJI), www.ceser.res.in/iji.html

  33. Kekre, H.B., Sarode, T.K.: Vector Quantized Codebook Optimization using K-Means. International Journal on Computer Science and Engineering (IJCSE) 1(3), 283–290 (2009), http://journals.indexcopernicus.com/abstracted.php?level=4&id_issue=839392

    Google Scholar 

  34. Kekre, H.B., Sarode, T.K.: 2-level Vector Quantization Method for Codebook Design using Kekre’s Median Codebook Generation Algorithm. Advances in Computational Sciences and Technology (ACST) 2(2), 167–178 (2009), http://www.ripublication.com/Volume/acstv2n2.htm

    Google Scholar 

  35. Kekre, H.B., Sarode, T.K.: Bi-level Vector Quantization Method for Codebook Generation. In: Second International Conference on Emerging Trends in Engineering and Technlogy, G. H. Raisoni College of Engineering, Nagpur, December 16-18 (2009) (this paper will be uploaded online at IEEE Xplore)

    Google Scholar 

  36. Clark, A.F.: The mini-MIAS database of mammograms, http://peipa.essex.ac.uk/info/mias.html (Last updated on July 31, 2003) (referred on 16-09-2009)

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Author information

Authors and Affiliations

  1. Senior Professor, MPSTME, NMIMS University, Vile-Parle (w), Mumbai, 56, India

    H. B. Kekre

  2. Ph.D. Scholar, MPSTME, NMIMS University, Assistant Prof., TSEC, Mumbai, 50, India

    Tanuja K. Sarode

  3. Ph.D. Scholar, MPSTME, NMIMS University, Lecturer, V.E.S.I.T, Mumbai, 400071, India

    Saylee M. Gharge

Authors
  1. H. B. Kekre
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  2. Tanuja K. Sarode
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  3. Saylee M. Gharge
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Editor information

Editors and Affiliations

  1. Engineers Network, Trivandrum, Kerala, India

    Vinu V Das

  2. NSS College of Engineering, Palakkadu, Kerala, India

    R. Vijayakumar

  3. Winona State University, Winona, MN, USA

    Narayan C. Debnath

  4. Viswajiyithi College Engineering, Muvattupuzha, Kerala, India

    Janahanlal Stephen

  5. Jackson State University, Jackson, MS, USA

    Natarajan Meghanathan

  6. University of West Indies, Kingston, Jamaica

    Suresh Sankaranarayanan

  7. CGM, ACEEE, Pattom, Kerala, India

    P. M. Thankachan

  8. University of Indonesia, Depok, Indonesia

    Ford Lumban Gaol

  9. College of Engineering, Trivandrum, Kerala, India

    Nessy Thankachan

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© 2010 Springer-Verlag Berlin Heidelberg

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Kekre, H.B., Sarode, T.K., Gharge, S.M. (2010). Vector Quantization for Tumor Demarcation of Mammograms. In: Das, V.V., et al. Information Processing and Management. BAIP 2010. Communications in Computer and Information Science, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12214-9_27

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  • DOI: https://doi.org/10.1007/978-3-642-12214-9_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12213-2

  • Online ISBN: 978-3-642-12214-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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