Abstract
As mammography moves towards completely digital and produces prohibitive amounts of data, compression plays an increasingly important role. Although current lossless compression methods provide very high-quality images, their compression ratios are very low. On the other hand, several lossy compression methods provide very high compression ratios but come with considerable loss of quality. In this work, we describe a novel compression method that consists of downsampling the mammograms before applying the encoding procedure, and applying super-resolution techniques after the decoding procedure to recover the original resolution image. In our experiments, we examine the tradeoffs between compression ratio and image quality using this scheme, and show it provides significant improvements over conventional methods.
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References
Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Computer Graphics and Applications 22(2), 56–65 (2002)
Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. International Journal on Compter Vision 40(1), 25–47 (2000)
Grinstead, B., Sari-Sarraf, H., Mitra, S., Gleason, S.: Content-based compression of mammograms for telecommunication and archiving. In: 13th IEEE Symposium on Computer-Based Medical Systems (CBMS 2000), p. 37 (2000)
Li, X., Shen, Y., Ma, J.: An efficient medical image compression scheme. In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, pp. 3437–3439 (January 2006)
Ravi Kumar, M.S., Koliwad, S., Dwarakish, G.S.: Lossless compression of digital mammography using fixed block segmentation and pixel grouping. In: Proceedings of indian conference on computer vision, graphics and image processing, pp. 201–206 (2008)
Perlmutter, S.M., Cosman, P.C., Gray, R.M., Olshen, R.A., Ikeda, D., Adams, C.N., Betts, B.J., Williams, M.B., Perlmutter, K.O., Aiyer, J.L., Fajardo, L., Birdwell, R., Daniel, B.L.: Image quality in lossy compressed digital mammograms. Signal processing 59(2), 189–210 (1997)
Persons, K., Palisson, P., Manduca, A., Erickson, B.J., Savcenko, V.: An analytical look at the effects of compression on medical images. Journal of Digital Imaging 10, 60–66 (1997)
Scharcanski, J.: Lossless and near-lossless compression for mammographic digital images. IEEE International Conference on Image Processing, 2253–2256 (2006)
Wang, X., Tang, X.: Hallucinating face by eigentransformation. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews (2005)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)
Wolberg, G.: 2-Pass Mesh Warping. In: Digital Image Warping, pp. 222–240. IEEE Computer Society Press, Los Alamitos (1990)
Zukoski, M.J., Boult, T., Iyriboz, T.: A novel approach to medical image compression. International Journal of Bioinformatics Research and Applications (2006)
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Zheng, J., Fuentes, O., Leung, MY., Jackson, E. (2010). Mammogram Compression Using Super-Resolution. In: MartÃ, J., Oliver, A., Freixenet, J., MartÃ, R. (eds) Digital Mammography. IWDM 2010. Lecture Notes in Computer Science, vol 6136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13666-5_7
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DOI: https://doi.org/10.1007/978-3-642-13666-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13665-8
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