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Deep Convolution Neural Network Based Denoiser for Mammographic Images

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Advances in Computing and Data Sciences (ICACDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1045))

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Abstract

Denoising is an important image pre-processing operation required to improve the image quality. In the past, several image denoising solutions have been put forth with varying performances. Recently, deep-learning based approaches have given better results than conventional algorithms. While these methods offer promising results on denoising of natural images, their application to medical imaging is yet to be fully explored. In this study, mammographic images, which are generally corrupted with Gaussian noise, have been effectively denoised using a deep convolution neural network. The model proposed in this work outshines various existing state-of-the-art solutions. Our model achieves a structural similarity index (SSIM) of 0.98 and value of 41.53 dB for peak signal to noise ratio (PSNR).

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Authors and Affiliations

  1. UIET, Panjab University, Chandigarh, India

    Gurprem Singh, Ajay Mittal & Naveen Aggarwal

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  1. Gurprem Singh
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  2. Ajay Mittal
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  3. Naveen Aggarwal
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Corresponding author

Correspondence to Gurprem Singh .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Computer Science and Engineering, Jaypee Institute of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. ÚTIA AV ČR, Institute of Information Theory and Automation, Prague 8, Praha, Czech Republic

    Jan Flusser

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. CSE Department, Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

    Rekha Kashyap

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Singh, G., Mittal, A., Aggarwal, N. (2019). Deep Convolution Neural Network Based Denoiser for Mammographic Images. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-13-9939-8_16

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  • DOI: https://doi.org/10.1007/978-981-13-9939-8_16

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  • Print ISBN: 978-981-13-9938-1

  • Online ISBN: 978-981-13-9939-8

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