Medical Image Denoising Using Convolutional Denoising Autoencoders

Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These...

Full description

Saved in:
Bibliographic Details
Published inIEEE ... International Conference on Data Mining workshops pp. 241 - 246
Main Author Gondara, Lovedeep
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Biomedical imaging
convolutional autoencoder
Convolutional codes
denoising autoencoder
Image denoising
Noise level
Noise measurement
Noise reduction
Training
Online AccessGet full text

Cover

More Information
Summary:Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.
ISSN:2375-9259
DOI:10.1109/ICDMW.2016.0041