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The Journal of Supercomputing

, Volume 75, Issue 2, pp 704–718 | Cite as

Medical image denoising using convolutional neural network: a residual learning approach

  • Worku Jifara
  • Feng JiangEmail author
  • Seungmin Rho
  • Maowei Cheng
  • Shaohui Liu
Article

Abstract

In medical imaging, denoising is very important for analysis of images, diagnosis and treatment of diseases. Currently, image denoising methods based on deep learning are effective, where the methods are however limited for the requirement of training sample size (i.e., not successful enough for small data size). Using small sample size, we design deep feed forward denoising convolutional neural networks by studying the model in deep framework, learning approach and regularization approach for medical image denoising. More specifically, we use residual learning as a learning approach and batch normalization as regularization in the deep model. Unlike most of the other image denoising approaches which directly learn the latent clean images, the residual learning approach learns the noise from the noisy images instead of the latent clean images where the denoised images are obtained by subtracting the learned residual from the noisy image. Moreover, batch normalization is integrated with residual learning to improve model learning accuracy and training time. We compute the quality of the reconstructed or denoised image in standard image quality metrics, peak signal to noise ratio and structural similarity and compare our model performance with some medical image denoising techniques. Experimental results reveal that our approach has better performance than some other methods.

Keywords

Medical image Image denoising Residual learning CNN Batch normalization 

Notes

Acknowledgements

This work is partially funded by the MOE-Microsoft Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, the Major State Basic Research Development Program of China (973 Program 2015CB351804) and the National Natural Science Foundation of China under Grant Nos. 61572155, 61672188 and 61272386. We would also like to acknowledge NVIDIA Corporation who kindly provided two sets of GPU. We would like to acknowledge the editors and the anonymous reviewers whose important comments and suggestions led to greatly improved the manuscript.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Worku Jifara
    • 1
  • Feng Jiang
    • 1
    Email author
  • Seungmin Rho
    • 2
  • Maowei Cheng
    • 3
  • Shaohui Liu
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Department of Media SoftwareSungkyul UniversityAnyangKorea
  3. 3.College of Command Information SystemPLA University of Science and TechnologyNanjingChina

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