Abstract
Research indicates out that early diagnosis of the tumor may increase the chance of disease treatment and currently, the most effective method for early detection of illness is mammography. For this reason, using techniques that result in better quality images, that is, with as little noise as possible can aid in the visualization and detection of lesions and improve the accuracy of the diagnosis. For this work were used three filters being them: Wiener, Lee and Frost and to measure its efficiency were used the following parameters: Peak Signal to Noise-Ratio (PSNR), Signal to Noise-Ratio (SNR) and Structural SIMilarity (SSIM). It was verified that the Wiener filter presented better performance when compared to the others.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pinotti, J.A., et al.: Breast cancer control program in developing countries. Breast Dis. 3(8), 243–250 (1995)
Rangayyana, R.M., Ayresa, F.J., Leo Desautels, J.E.: A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. J. Frankl. Inst. 344(3/4), 312–348 (2007)
Cheng, H., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of micro-calcifications in mammograms: a survey. Pattern Recogn. 36(12), 2967–2991 (2003)
Thangavel, K., Karnan, M., Sivakumar, R., Mohideen, A.K.: Automatic detection of microcalcification in mammograms: a review. Graph. Vis. Image Process. (2008)
Mencattini, A., et al.: Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans. Instrum. Meas. 57(7), 1422–1430 (2008)
Yadav, A., Singh, B., Singh, S.: Comparative analysis of different enhancement method on digital mammograms. In: 2011 2nd International Conference on Computer and Communication Technology (ICCCT), pp. 159–163. IEEE (2011)
Moreira, I.C., et al.: Inbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)
Freer, P.E.: Mammographic breast density: impact on breast cancer risk and implications for screening. Radiographics 35(2), 302–315 (2015)
Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11(11), 1260–1270 (2002)
Bankman, I. (ed.).: Handbook of Medical Image Processing and Analysis. Elsevier (2008)
Amoako-Yirenkyi, P., Appati, J.K., Dontwi, I.K.: A new construction of a fractional derivative mask for image edge analysis based on Riemann-Liouville fractional derivative. Adv. Differ. Equ. 2016(1), 238 (2016)
Bushberg, J.T., Boone, J.M.: The Essential Physics of Medical Imaging. Lippincott Williams & Wilkins (2011)
Bandeira, B., et al.: Comparação entre diferentes métodos de obtenção da razão sinal-ruído em imagens por ressonância magnética. Revista Brasileira de Física Médica 10(2), 25–28 (2016)
Wang, Z., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Rosa, R.G.S.: Filtragem de ruído speckle em imagens clínicas de ecografia. 2012. Tese de Doutorado. Instituto Politécnico de Bragança, Escola Superior de Tecnologia e Gestão
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
da Costa Junior, C.A., Patrocinio, A.C. (2019). Performance Evaluation of Denoising Techniques Applied to Mammograms of Dense Breasts. In: Costa-Felix, R., Machado, J., Alvarenga, A. (eds) XXVI Brazilian Congress on Biomedical Engineering. IFMBE Proceedings, vol 70/2. Springer, Singapore. https://doi.org/10.1007/978-981-13-2517-5_56
Download citation
DOI: https://doi.org/10.1007/978-981-13-2517-5_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2516-8
Online ISBN: 978-981-13-2517-5
eBook Packages: EngineeringEngineering (R0)