Abstract
Mammography is one of the most effective techniques for early detection of breast cancer. The quality of the image may suffer from poor resolution or low contrast, which can effect the efficiency of radiologists. In order to improve the visual quality of mammograms, this paper introduces a new mammographic image enhancement algorithm. Firstly an intensity based nonlinear transformation is used for reducing the background tissue intensity, and secondly adaptive local contrast enhancement is realized based on local standard deviation and luminance information. The proposed method can obtain improved performance compared to alternative methods both covering objective and subjective aspects, based on 45 images. Experimental results demonstrate that the proposed algorithm can improve the contrast effectively and enhance lesion information (microcalcifications and/or masses).
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References
Tao, L., Asari, V.K.: Adaptive and integrated neighborhood-dependent approach for nonlinear enhancement of color images. J. Electron. Imaging 14(4), 781–792 (2005)
Suckling, J., Parker, J., Dance, D.R., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S.L., Taylor, P., Betal, D., Savage, J.: The mammographic image analysis society digital mammogram database. Excerpta Med. Int. Congr. Ser. 1069, 375–378 (1994)
Japanese Society of Medical Imaging Technology. http://www.jamit.jp/cad-committe/caddbinfo. Accessed 11 Jan 2012
Guo, M., Dong, M., Wang, Z., Ma, Y.: A new method for mammographic mass segmentation based on parametric active contour model. In: Proceedings of IEEE Wavelet Analysis and Pattern Recognition Conference, pp. 27–33 (2015)
Pisano, E.D., Zong, S., Hemminger, B.M., Deluca, M., Johnston, R.E.: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J. Digit. Imaging 11(4), 193–200 (1998)
Karen, P., Yicong, Z., Sos, A., Hongwei, J.: Nonlinear unsharp masking for mammogram enhancement. IEEE Trans. Inf Technol. Biomed. 15(6), 918–928 (2011)
Hari, V.S., Gopikakumari, R.: Unsharp masking using quadratic filter for the enhancement of fingerprints in noisy background. Pattern Recogn. 46(12), 3198–3207 (2013)
Ramponi, G., Polesel, A.: Rational unsharp masking technique. J. Electron. Imaging 7(2), 333–338 (1998)
Tao, L., Asari, V.K.: Adaptive and integrated neighborhood-dependent approach for nonlinear enhancement of color images. J. Electron. Imaging 14(4), 781–792 (2005)
Acknowledgment
This work is jointly supported by the National Natural Science Foundation of China (nos. 61175012 and 61201421), Natural Science Foundation of Gansu Province (nos. 145RJZA181), and the Fundamental Research Funds for the Central Universities of China (nos. lzujbky-2013-k06 and lzujbky-2015-196).
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Ding, C., Dong, M., Zhang, H., Ma, Y., Yan, Y., Zwiggelaar, R. (2016). Nonlinear Local Transformation Based Mammographic Image Enhancement. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_22
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DOI: https://doi.org/10.1007/978-3-319-41546-8_22
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