Lossless compression of digital mammography using base switching method


Mammography is a specific type of imaging that uses low-dose x-ray system to examine breasts. This is an efficient means of early detection of breast cancer. Archiving and retaining these data for at least three years is expensive, diffi-cult and requires sophisticated data compres-sion techniques. We propose a lossless com-pression method that makes use of the smoothness property of the images. In the first step, de-correlation of the given image is done using two efficient predictors. The two residue images are partitioned into non overlapping sub-images of size 4x4. At every instant one of the sub-images is selected and sent for coding. The sub-images with all zero pixels are identi-fied using one bit code. The remaining sub- images are coded by using base switching method. Special techniques are used to save the overhead information. Experimental results indicate an average compression ratio of 6.44 for the selected database.

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Mulemajalu, R. and Koliwad, S. (2009) Lossless compression of digital mammography using base switching method. Journal of Biomedical Science and Engineering, 2, 336-344. doi: 10.4236/jbise.2009.25049.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Saxena, S., Rekhi, B., Bansal, A., Bagya, A., Chintamani, and Murthy, N. S., (2005) Clinico-morphological patterns of breast cancer including family history in a New Delhi Hospital, In-dia—a cross sectional study, World Journal of Surgical Oncol-ogy, 1–8.
[2] Cahoon, T. C., Sulton, M. A, and Bezdek, J. C. (2000), Breast cancer detection using image processing techniques, The 9th IEEE International Conference on Fuzzy Systems, 2, 973–976.
[3] Penedo, M., Pearlman, W. A., Tahoces, P. G., Souto, M., and Vidal, J. J., (2003) Region-based wavelet coding methods for digital mammography, IEEE Transactions on Medical Imaging, 22, 1288–1296.
[4] Wu, X. L., (1997), Efficient lossless compression of Continu-ous-tone images via context selection and quantization, IEEE Transaction. on Image Processing, 6, 656– 664.
[5] Ratakonda, K. and Ahuja, N. (2002), Lossless image compres-sion with multi-scale segmentation, IEEE Transactions on Im-age Processing, 11, 1228–1237.
[6] Weinberger, M. J., Rissanen, J., and Asps, R., (1996) Applica-tion of universal context modeling to lossless compression of gray scale images, IEEE Transaction on Image Processing, 5, 575–586.
[7] Grecos, C., Jiang, J., and Edirisinghe, E. A., (2001) Two Low cost algorithms for improved edge detection in JPEG-LS, IEEE Transactions on Consumer Electronics, 47, 466–472.
[8] Weinberger, M. J., Seroussi, G., and Sapiro, G., (2000) The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS, IEEE Transactions on Image processing, 9, 1309–1324.
[9] Sung, M. M., Kim, H.-J., Kim, E.-K., Kwak, J.-Y., Kyung, J., and Yoo, H.-S., (2002) Clinical evaluation of JPEG 2000 com-pression for digital mammography, IEEE Transactions on Nu-clear Science, 49, 827–832.
[10] Neekabadi, A., Samavi, S., and Karimi, N., (2007) Lossless compression of mammographic images by chronological sift-ing of prediction errors, IEEE Pacific Rim Conference on Communications, Computers & Signal Processing, 58–61.
[11] Li, X., Krishnan, S., and Marwan, N. W., (2004) A novel way of lossless compression of digital mammograms using gram-mar codes, Canadian Conference on Electrical and Computer Engineering, 4, 2085–2088.
[12] da Silva, L. S. and Scharcanski, J., (2005) A lossless compres-sion approach for mammographic digital images based on the delaunay triangulation, International Conference on Image Processing, 2, 758–761.
[13] Khademi, A. and Krishnan, S., (2005) Comparison of JPEG2000 and other lossless vompression dchemes for figital mammograms, Proceedings of the IEEE Engineering in Medi-cine and Biology Conference, 3771– 3774.
[14] Shen, L. and Rangayyan, R. M., (1997) A segmentation based lossless image coding method for high-resolution medical im-age compression, IEEE Transactions on Medical imaging, 16, 301–307.
[15] Ranganathan, N., Romaniuk, S. G., and Namuduri, K. R., (1995) A lossless image compression algorithm using variable block segmentation, IEEE Transactions on Image Processing, 4, 1396–1406.
[16] Namuduri, K. R., Ranganathan, N., and Rashedi, H., (1996) SVBS: A high-resolution medical image compression algo-rithm using slicing with variable block size segmentation, IEEE Proceedings of ICPR, 919–923.
[17] Alsaiegh, M. Y. and Krishnan, S. (2001), Fixed block- based lossless compression of digital mammograms, Canadian Con-ference on Electrical and Computer Engineering, 2, 937–942.
[18] Chuang, T.-J. and Lin, J. C., (1998) A new algorithm for loss-less still image compression, Pattern Recognition, 31, 1343–1352.
[19] Chang, C.-C., Hsieh, C.-P., and Hsiao, J.-Y., (2003) A new approach to lossless image compression, Proceedings of ICCT’03, 1734–38.
[20] Ravikumar, M. S., Koliwad, S., and Dwarakish, G. S., (2008) Lossless compression of digital mammography using fixed block segmentation and pixel grouping, Proceedings of IEEE 6th Indian Conference on Computer Vision Graphics and Im-age Processing, 201–206.
[21] Sayood, K., (2003) Lossless compression handbook, First edi-tion, Academic Press, USA, 207–223.

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