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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Deep Vectorization Convolutional Neural Networks for Denoising in Mammogram Using Enhanced Image

verfasst von : Varakorn Kidsumran, Yalin Zheng

Erschienen in: Medical Image Understanding and Analysis

Verlag: Springer International Publishing

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Abstract

Mammography is an X-ray image of the breast which has been widely used for the management of breast cancer. However, in many cases, it is not easy to identify a sign of cancer as tumour or malignancy due to clouding various noise patterns caused by the low dose radiation from the X-ray machine. Mammogram denoising is an important process to improve the visual quality of mammogram to help the radiologist’s diagnosis when they screening mammogram. This paper introduces denoising deep vectorization convolutional neural networks using an enhanced image from direct contrast in a wavelet domain for training. Then, Denoised mammogram is obtained from mapping between the original and enhanced image. Mammogram image from the mini-MIAS database of mammograms was used in this experiment. The experimental results demonstrate that the proposed method can effectively suppress various noises in mammogram both qualitative and subjective test by comparison to traditional denoising methods.

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Vorheriges Kapitel A Neural Network Approach for Image Reconstruction from a Single X-Ray Projection
Nächstes Kapitel A Hybrid Machine Learning Approach Using LBP Descriptor and PCA for Age-Related Macular Degeneration Classification in OCTA Images
Literatur
1.
2.
3.
Heinlein, P., Drexl, J., Schneider, W.: Integrated wavelets for enhancement of micro calcifications in digital mammography. IEEE Trans. Med. Imaging 22(3), 402–413 (2003)CrossRef
4.
Mencattini, A., Rabottino, G., Salmeri, M., Sciunzi, B., Lojacono, R.: Denoising and enhancement of mammmographic images under the assumption of heteroscedastic additive noise by an optimal subband thresholding. Int. J. Wavelets Multiresolut. Inf. Process. 8, 713–741 (2010)MathSciNetCrossRef
5.
Elsherif, M.S., Elsayad, A.: Wavelet packet denoising for mammogram enhancement. Circuits Syst. 1, 180–183 (2001)
6.
Matsuyama, E., Tsai, D.Y., Lee, Y., Tsurumaki, M.: A modified undecimated discrete wavelet transform based approach to mammographic image denoising. J. Digit. Imaging 26, 748–758 (2013)CrossRef
7.
Starck, J.L., Fadili, J., Murtagh, F.: The undecimated wavelet decomposition and its reconstruction. IEEE Trans. Image Process. 16(2), 297–309 (2007)MathSciNetCrossRef
8.
Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4, 490–530 (2005)MathSciNetCrossRef
9.
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007)MathSciNetCrossRef
10.
Eksioglu, E.M.: Decoupled algorithm for MRI reconstruction using nonlocal block matching model: BM3D-MRI. J. Math. Imaging Vis. 56, 430–440 (2016)MathSciNetCrossRef
11.
Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862–2869 (2014)
12.
Luisier, F., Blu, T., Unser, M.: A new SURE approach to image denoising: interscale orthonormal wavelet thresholding. IEEE Trans. Image Process. 16, 593–606 (2007)MathSciNetCrossRef
13.
Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9, 1532–1546 (2000)MathSciNetCrossRef
14.
Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P.: Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans. Image Process. 12, 1338–1351 (2003)MathSciNetCrossRef
15.
Blu, T., Luisier, F.: The SURE-LET approach to image denoising. IEEE Trans. Image Process. 16, 2778–2786 (2007)MathSciNetCrossRef
16.
Matsuyama, E.: SURE-LET image denoising with directional LOTS. In: Picture Coding Symposium, pp. 232–239 (2012)
17.
Wang, J., Wang, Y., Li, Y., Liu, J.: Improved median filtering denoising algorithm and analysis. In: International Conference on Information Science and Control Engineering (IET) (2012)
18.
Bhateja, V., Rastogi, K., Verma, A., Malhotra, C.: A non-iterative adaptive median filter for image denoising. In: International Conference on Signal Processing and Integrated Networks (SPIN), pp. 113–118 (2014)
19.
Wu, S., Chen, H., Xu, X., Long, H., Jiang, W., Xu, D.: An improved median filter algorithm based on VC in image denoising. In: 10th International Conference on Computational Intelligence and Security (CIS), pp. 193–196 (2014)
20.
Zhang, X., Cheng, S., Ding, H., Wu, H., Gong, N., Cheng, R.: Ultrasound medical image denoising based on multi-direction median filter. In: 8th International Conference on Information Technology in Medicine and Education (ITME), pp. 835–839 (2016)
21.
Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds. arXiv:​1211.​1544 (2012)
22.
Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms. arXiv:​1211.​1552. (2012)
23.
Jain, V., Seung, S.: Natural image denoising with convolutional networks. In: Advances Neural Information Processing Systems, pp. 769–776 (2009)
24.
Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances Neural Information Processing Systems, pp. 341–349 (2012)
25.
Agostinelli, F., Anderson, M.R., Lee, H.: Adaptive multi-column deep neural networks with application to robust image denoising. In: Advances in Neural Information Processing Systems, vol. 26, pp. 1493–1501 (2013)
26.
Gondara, L.: Medical image denoising using convolutional denoising autoencoders. In: IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 241–246 (2016)
27.
Ren, J., Xu, L.: On vectorization of deep convolutional neural networks for vision tasks. In: AAAI, pp. 1840–1846 (2015)
28.
Suckling, J., et al.: The mammographic image analysis society digital mammogram exerpta media. In: International Congress Sersis, vol. 1069, pp. 375–378 (1994)
Metadaten
Titel
Deep Vectorization Convolutional Neural Networks for Denoising in Mammogram Using Enhanced Image
verfasst von
Varakorn Kidsumran
Yalin Zheng
Copyright-Jahr
2020
Buch
Medical Image Understanding and Analysis

Print ISBN: 978-3-030-39342-7

Electronic ISBN: 978-3-030-39343-4

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-39343-4_19