论文信息 - Medical Image De-noising Using Deep Networks

Medical Image De-noising Using Deep Networks

Noise Removal in medical images is a requisite pre-processing step for medical image analysis. In this paper, we present three deep learning architectures: Convolutional Auto-encoders, U-Net and a Double U-Net architecture utilizing convolution layers for medical Image De-noising. We use PSNR(Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Metric) to compare the results of the networks.In our experiments, the variant of the U-Net architecture implemented performed better than the two architectures in terms of its SSIM.

Andrzej Cichocki | Maame G. Asante-Mensah | A. Cichocki

[1] Kyunghyun Cho,et al. Boltzmann Machines and Denoising Autoencoders for Image Denoising , 2013, ICLR.

[2] Ian W. Ricketts,et al. The Mammographic Image Analysis Society digital mammogram database , 1994 .

[3] Shaohui Liu,et al. Medical image denoising using convolutional neural network: a residual learning approach , 2017, The Journal of Supercomputing.

[4] Jürgen Schmidhuber,et al. Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.

[5] Enhong Chen,et al. Image Denoising and Inpainting with Deep Neural Networks , 2012, NIPS.

[6] Wei Xiong,et al. Stacked Convolutional Denoising Auto-Encoders for Feature Representation , 2017, IEEE Transactions on Cybernetics.

[7] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[8] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[9] M. Bertero,et al. Image deblurring with Poisson data: from cells to galaxies , 2009 .

[10] Yu-Bin Yang,et al. Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections , 2016, ArXiv.

[11] Lovedeep Gondara,et al. Medical Image Denoising Using Convolutional Denoising Autoencoders , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[12] Timothy F. Cootes,et al. A benchmark for comparison of dental radiography analysis algorithms , 2016, Medical Image Anal..