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Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval

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Chandy, D. Abraham [Karunya University, Coimbatore, India] Suviseshamuthu, Easter Selvan [UCL] Johnson, J. Stanly [Control System and Instrumentation, Saudi Kayan, Saudi Arabia]

Texture is one of the visual contents of an image used in content-based image retrieval (CBIR) to represent and index the image. Statistical textural representation methods characterize texture by the statistical distribution of the image intensity. This paper proposes a gray level statistical matrix from which four statistical texture features are estimated for the retrieval of mammograms from mammographic image analysis society (MIAS) database. The mammograms comprising architectural distortion, asymmetry, calcification, circumscribed, ill-defined, spiculated and normal classes are used in the experimentation. Precision, recall, retrieval rate, normalized average rank, average matching fraction, storage requirement and retrieval time are the performance measures used for the evaluation of retrieval performance. Using the proposed method, the highest mean precision rate obtained is 85.1 %. The results show that the proposed method outperforms the state-of-the-art texture feature extraction methods in mammogram retrieval problem. © 2013 Springer Science+Business Media New York.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès restreint
Publication date 2014
Language Anglais
Journal information "Multimedia Tools and Applications : an international journal" - Vol. 72, no. 2, p. 2011-2024 (2014)
Peer reviewed yes
Publisher Springer New York LLC
e-issn 1573-7721
issn 1380-7501
Publication status Publié
Affiliation UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique
Links
  1. Cheng H.D., Shi X.J., Min R., Hu L.M., Cai X.P., Du H.N., Approaches for automated detection and classification of masses in mammograms, 10.1016/j.patcog.2005.07.006
  2. Chen CH, Pau LF, Wang PSP (eds) (1998) The handbook of pattern recognition and computer vision, (2nd edn). World Scientific Publishing pp 207–248
  3. Choraś RS (2008) Feature extraction for classification and retrieval mammogram in databases. Int J Med Eng Inf 1(1):50–61
  4. Do M.N., Vetterli M., Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance, 10.1109/83.982822
  5. Eisa M, Refaat M, El-Gamal AF (2009) Preliminary diagnostics of mammograms using moments and texture features. ICGST-GVIP J 9(5):21–27
  6. El-Naqa I., Yang Y., Galatsanos N.P., Nishikawa R.M., Wernick M.N., A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography, 10.1109/tmi.2004.834601
  7. Felipe JC, Traina AJM, Ribeiro MX, Souza EPM, Junior CT (2006) Effective shape-based retrieval and classification of mammograms. In: Proceedings of the Twenty First Annual ACM symposium on Applied Computing. pp 250–255
  8. Greenspan Hayit, Pinhas Adi T., Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework, 10.1109/titb.2006.874191
  9. Haralick Robert M., Shanmugam K., Dinstein Its'Hak, Textural Features for Image Classification, 10.1109/tsmc.1973.4309314
  10. Khotanzad A., Hong Y.H., Invariant image recognition by Zernike moments, 10.1109/34.55109
  11. Korn P., Sidiropoulos N., Faloutsos C., Siegel E., Protopapas Z., Fast and effective retrieval of medical tumor shapes, 10.1109/69.738356
  12. Kwitt R, Meerwald P, Uhl A, Efficient Texture Image Retrieval Using Copulas in a Bayesian Framework, 10.1109/tip.2011.2108663
  13. Lamard M, Cazuguel G, Quellec G, Bekri L, Roux C, Cochener B (2007) Content-based image retrieval based on wavelet transform coefficients distribution. In: Proceedings of the Twenty Ninth Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Press, Lyon, France, pp 4532–4535
  14. Lu S, Bottema MJ (2003). Structural image texture and early detection of breast cancer. In: Proceedings of the 2003 APRS Workshop on Digital Image Computing. pp 15–20
  15. Manjunath B.S., Ma W.Y., Texture features for browsing and retrieval of image data, 10.1109/34.531803
  16. Desautels J.E.L., Rangayyan R., Mudigonda N.R., Gradient and texture analysis for the classification of mammographic masses, 10.1109/42.887618
  17. Müller Henning, Michoux Nicolas, Bandon David, Geissbuhler Antoine, A review of content-based image retrieval systems in medical applications—clinical benefits and future directions, 10.1016/j.ijmedinf.2003.11.024
  18. Müller Henning, Müller Wolfgang, Squire David McG., Marchand-Maillet Stéphane, Pun Thierry, Performance evaluation in content-based image retrieval: overview and proposals, 10.1016/s0167-8655(00)00118-5
  19. Pandey D, Kumar R (2011) Inter space local binary patterns for image indexing and retrieval. J Theor Appl Inf Technol 32(2):160–168
  20. Qin X, Yang Y (2004) Similarity measure and learning with Gray Level Aura Matrices (GLAM) for texture image retrieval. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit Washington DC USA 1:326–333
  21. Quellec G., Lamard M., Cazuguel G., Cochener B., Roux C., Wavelet optimization for content-based image retrieval in medical databases, 10.1016/j.media.2009.11.004
  22. Rogers Wendy A., Veale Bronwyn, Dollars, debts and duties: lessons from funding Australian general practice, 10.1046/j.1365-2524.2000.00253.x
  23. Smeulders A.W.M., Worring M., Santini S., Gupta A., Jain R., Content-based image retrieval at the end of the early years, 10.1109/34.895972
  24. Srinivasan GN, Shobha G (2008) Statistical texture analysis. Proc World Acad Sci Eng Technol 36:1264–1269
  25. Suckling J, Parker J, Dance DR, Astley SM, Hutt I, Boggis CRM, Ricketts I, Stamatakis E, Cerneaz N, Kok SL, Taylor P, Betal D, Savage J (1994) Mammographic image analysis society digital mammogram database. Proceedings of International Workshop on Digital Mammography pp 211–221
  26. Sun J, Zhang Z (2008) An effective method for mammograph image retrieval. In: Proceedings of International Conference on Computational Intelligence and Security. pp 190–193
  27. Tourassi Georgia D., Journey toward Computer-aided Diagnosis: Role of Image Texture Analysis, 10.1148/radiology.213.2.r99nv49317
  28. Tourassi Georgia D., Harrawood Brian, Singh Swatee, Lo Joseph Y., Floyd Carey E., Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms : Information-theoretic similarity measures for retrieval and detection, 10.1118/1.2401667
  29. Wei CH, Li CT, Wilson R (2005) A general framework for content-based medical image retrieval with its application to mammogram retrieval. Proc SPIE Int Symp Med Imaging 5748:134–143
  30. Wei CH, Li CT, Wilson R (2006) A content-based approach to medical image database retrieval. In: Ma ZM (ed) Database modeling for industrial data management: emerging technologies and applications. Idea Group Publishing, Hershey, pp 258–291
  31. Wiesmuller S, Chandy DA (2010) Content-based mammogram retrieval using gray level aura matrix. Int J Comput Commun Inf Syst (IJCCIS) 2(1):217–222
Bibliographic reference Chandy, D. Abraham ; Suviseshamuthu, Easter Selvan ; Johnson, J. Stanly. Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval. In: Multimedia Tools and Applications : an international journal, Vol. 72, no. 2, p. 2011-2024 (2014)
Permanent URL http://hdl.handle.net/2078.1/159615