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Using Autoencoders for Mammogram Compression

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Abstract

This paper presents the results obtained for medical image compression using autoencoder neural networks. Since mammograms (medical images) are usually of big sizes, training of autoencoders becomes extremely tedious and difficult if the whole image is used for training. We show in this paper that the autoencoders can be trained successfully by using image patches instead of the whole image. The compression performances of different types of autoencoders are compared based on two parameters, namely mean square error and structural similarity index. It is found from the experimental results that the autoencoder which does not use Restricted Boltzmann Machine pre-training yields better results than those which use this pre-training method.

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References

  1. Hinton, G. E., and Salakhutdinov, R. R., Reducing the dimensionality of data with neural networks. Science 313:504–507, 2006.

    Article  MathSciNet  Google Scholar 

  2. Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H., Greedy layer-wise training of deep networks. In: Advances in Neural Information Processing Systems, Tech. Rep. 1282. Vol. 19. Cambridge, MA: MIT Press, 2006.

    Google Scholar 

  3. Polak, E., and Ribire, G., Note sur la convergence de methods de directions conjures. Revue Francais Information Recherche Operationnelle 16:35–43, 1969.

    Google Scholar 

  4. Larochelle, H., Erhan, D., Courville, A., Bergstra, J., and Bengio, Y., An empirical evaluation of deep architectures on problems with many factors of variation. In: Proceedings of the 24th International Conference on Machine Learning (ICLM). pp. 473–480. Corvalis, OR: ACM, 2007.

    Chapter  Google Scholar 

  5. Hinton, G. E., Boltzmann Machine. Scholarpedia 2(5):1668, 2007. Available: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5586.

    Google Scholar 

  6. Tee, Y. W., and Hinton, G. E., Rate-coded Restricted Boltzmann machines for face recognition. In: Neural Information Processing Systems. Vol. 13, pp. 908–914. Cambridge, MA: MIT Press, 2000.

    Google Scholar 

  7. Salakhutdinov, R., Mnih, A., and Hinton, G., Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning. pp. 791–798. Corvallis, OR, 2007.

  8. Cosman, P. C., Gray, R. M., and Olshen, R. A., Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy. Proc. of the IEEE 82(6):919–932, 1994.

    Article  Google Scholar 

  9. Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P., Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process 3(4):600–612, 2004.

    Article  Google Scholar 

  10. Lundstrom, C., Measuring digital image quality. In: Tech. Rep. 1, Department of Science and Technology, Linkoping University. Available: http://urn.kb.se/resolve?urn=urn:nbn: se:liu:diva-5586.

  11. Heath, M., Bowyer, K., Kopans, D., Moore, R., and Kegelmeyer, W. P., The digital database for screening mammography. In: Yaffe, M. J. (Ed.), Proc. of the Fifth Int. Workshop on Digital Mammography. pp. 212–218. Madison, WI: Medical Physics Publishing, 2001.

    Google Scholar 

  12. Hinton, G. E., and Salakhutdinov, R. R., Supporting online material for reducing the dimensionality of data with neural networks. Science 313(5786), 2006. Available: www.sciencemag.org/cgi/content/full/313/5786/504/DC1.

  13. Steudel, A., Ortmann, S., and Glesner, M., Medical image compression with neural nets. In: Proc. of ISUMA - NAFIPS ’95. pp. 571–576, 1995.

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Authors and Affiliations

  1. Faculty of Information Technology, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    Chun Chet Tan & Chikkannan Eswaran

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  1. Chun Chet Tan
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  2. Chikkannan Eswaran
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Correspondence to Chun Chet Tan.

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Tan, C.C., Eswaran, C. Using Autoencoders for Mammogram Compression. J Med Syst 35, 49–58 (2011). https://doi.org/10.1007/s10916-009-9340-3

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  • DOI: https://doi.org/10.1007/s10916-009-9340-3

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