Texture Features Extraction in Mammograms Using Non-Shannon Entropies

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 68)

Abstract

This paper deals with the problem of texture-features-extraction in digital mammograms using non-Shannon measures of entropy. Texture-features-extraction is normally achieved using statistical texture-analysis method based on gray-level histogram moments. Entropy is important texture feature to measure the randomness of intensity distribution in a digital image. Generally, Shannon’s measure of entropy is employed in various feature-descriptors implemented so far. These feature-descriptors are used for the purpose of making a distinction between normal and abnormal regions in mammograms. As non-Shannon entropies have a higher dynamic range than Shannon’s entropy covering much wider range of scattering conditions, they are more useful in estimating scatter density and regularity. Based on these considerations, an attempt is made to develop a new type of feature-descriptor using non-Shannon’s measures of entropy for classifying normal and abnormal mammograms. Experiments are conducted on images of mini-MIAS (Mammogram Image Analysis Society) database to examine its effectiveness. The results of this study are quite promising for extending the work towards the development of a complete Computer Aided Diagnosis (CAD) system for early detection of breast cancer.

Keywords

High Dynamic Range Digital Mammogram Markov Random Field Model Gray Level Histogram Mammogram Image 
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.

References

  1. 1.
    H.S. Sheshadri, A. Kandaswamy, Experimental investigation on breast tissue classification based on statistical feature extraction of mammogram, computerized. Med. Imaging Graph. 31, 46–48 (2007)CrossRefGoogle Scholar
  2. 2.
    I. Christoyianni, A. Koutras, E. Dermatas, G. Kokkinakis, Computer aided diagnosis of breast cancer in digitized mammograms. Comput. Med. Imaging Graph. 26, 309–319 (2002)CrossRefGoogle Scholar
  3. 3.
    B. Verma, P. Zhang, A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms. Appl. Soft Comput. l(7), 612–625 (2007)CrossRefGoogle Scholar
  4. 4.
    S.-K. Lee, C.-S. Lo, C.-M. Wang, P.-C. Chung, C.-I. Chang, C.-W. Yang, P.-C. Hsu, A computer-aided design mammography screening system for detection and classification of microcalcifications. Int. J. Med. Inf. 60, 29–57 (2000)CrossRefGoogle Scholar
  5. 5.
    H.D. Cheng, X. Cai, X. Chen, L. Hu, X. Lou, Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recogn. 36, 2967–2991 (2003)MATHCrossRefGoogle Scholar
  6. 6.
    H.D. Cheng, M. Cui, Mass lesion detection with a fuzzy neural network. Pattern Recogn. 37, 1189–1200 (2004)CrossRefGoogle Scholar
  7. 7.
    H.D. Cheng, X.J. Shi, R. Min, L.M. Hu, X. P. Cai, H.N. Du, Approaches for automated detection and classification of masses in mammograms. Pattern Recogn. 39, 646–668 (2006)CrossRefGoogle Scholar
  8. 8.
    M.E. Mavroforakis, H.V. Georgiou, N. Dimitropoulos, D. Cavouras, S. Theodoridis, Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. Artif. Int. Med. 37, 145–162 (2006)CrossRefGoogle Scholar
  9. 9.
    B. Verma, J. Zakos, A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans. Inf. Technol. Biomed. 5(1), (Mar 2001)Google Scholar
  10. 10.
    J.K. Kim, H.W. Park, Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Trans. Med. Imaging 18(3), (Mar 1999)Google Scholar
  11. 11.
    J.K. Kim, J. Mi Park, K.S. Song, H.W. Park, Texture analysis and artificial neural network for detection of clustered microcalcifications on mammograms, in Neural Networks for Signal Processing [1997] VII, Proceedings of the 1997 IEEE Workshop, Amelia Island, Florida, USA, pp. 199–206Google Scholar
  12. 12.
    A Karahaliou, S Skiadopoulos, I Boniatis, P Sakellaropoulos, E Likaki, G Panayiotakis, L Costaridou, Texture analysis microcalcifications on mammograms for breast cancer diagnosis. Brit. J. Radiol. 80, 648–656 (2007)CrossRefGoogle Scholar
  13. 13.
    Y.-Y. Wan, J.-X. Du, D.-S. Huang, Z. Chi, Y.-M. Cheung, X.-F. Wang, G.-J. Zhang, Bark texture feature extraction based on statistical texture analysis, in Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, Oct 20–22, 2004Google Scholar
  14. 14.
    M.R. Chandraratne, Comparison of three statistical texture measures for lamb grading, in 1st International Conference on Industrial and Information Systems,ICIIS2006 Sri Lanka, 8–11 Aug 2006Google Scholar
  15. 15.
    J.N. Kapur, Measure of Information and Their Applications (Wiley Eastern Limited, New Delhi, 1994)Google Scholar
  16. 16.
    L.I. Yan, F. Xiaoping, L. Gang, An application of Tsallis entropy minimum difference on image segmentation, in Proceeding of the 6th World Congress on Intelligent Control and Automation, Dalian, China, 21–23 June 2006Google Scholar
  17. 17.
    M. Portes de Albuquerque, I.A. Esquef, A.R. Gesualdi Mello, Image thresholding using Tsallis entropy. Pattern Recogn. Lett. 25(2004), 1059–1065 (2004)CrossRefGoogle Scholar
  18. 18.
    R. Smolikova, M.P. Wachowiak, G.D. Tourassi, A. Elmaghraby, J.M. Zurada, Characterization of ultrasonic back scatter based on generalized entropy, in Proceeding of the 2nd Joint EMBS/BMES Conference, Houston, TX, USA, 23–26 Oct 2002Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Electronics & Communication EngineeringSLIET, LongowalSangrurIndia

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