Survey of image denoising methods for medical image classification

Peter F. Michael; Hong-Jun Yoon

doi:10.1117/12.2549695

16 March 2020 Survey of image denoising methods for medical image classification

Peter F. Michael, Hong-Jun Yoon

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113143R (2020) https://doi.org/10.1117/12.2549695
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Medical imaging devices, such as X-ray machines, inherently produce images that suffer from visual noise. Our objectives were to (i.) determine the effect of image denoising on a medical image classification task, and (ii.) determine if there exists a correlation between image denoising performance and medical image classification performance. We performed the medical image classification task on chest X-rays using the DenseNet-121 convolutional neural network (CNN) and used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics as the image denoising performance measures. We first found that different denoising methods can make a statistically significant difference in classification performance for select labels. We also found that denoising methods affect fine-tuned models more than randomly-initialized models and that fine-tuned models have significantly higher and more uniform performance than randomly-initialized models. Lastly, we found that there is no significant correlation between PSNR and SSIM values and classification performance for our task.

Citation Download Citation

Peter F. Michael and Hong-Jun Yoon "Survey of image denoising methods for medical image classification", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143R (16 March 2020); https://doi.org/10.1117/12.2549695

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available