Denoising method for image quality improvement in photoacoustic microscopy using deep learning

Ziming Yu; Kanggao Tang; Xianlin Song

doi:10.1117/12.2600759

21 September 2021 Denoising method for image quality improvement in photoacoustic microscopy using deep learning

Ziming Yu, Kanggao Tang, Xianlin Song

Proceedings Volume 11875, Computational Optics 2021; 118750A (2021) https://doi.org/10.1117/12.2600759
Event: SPIE Optical Systems Design, 2021, Online Only

Abstract

Photoacoustic microscopy (PAM) is an imaging technology developed rapidly in recent years. The technology has the advantages of high resolution, rich contrast of optical imaging and high penetration depth of acoustic imaging. It is widely used in biomedical field, such as tumor detection. Photoacoustic images can not only reflect the structural characteristics of tissues, but also reflect the metabolic state, disease characteristics and even nerve activity of tissues, so as to realize functional imaging. Photoacoustic (PA) signals are inherently recorded in noisy environments and are also exposed to the noise of system components. The presence of noise has a great negative impact on image quality and interferes with image details. Therefore, it is necessary to reduce the noise in PA signals to reconstruct images with less interference information. Because deep learning can process image information quickly and efficiently, deep learning has become the preferred method for photoacoustic image denoising in recent years. In this study, the photoacoustic blood vessel image obtained was added with a certain intensity of Gaussian noise, and the denoising generative adversarial network based on Wasserstein distance (WGAN) was used to denoise the photoacoustic blood image. For the purpose of evaluation, the Peak Signal-to-Noise Ratio (RSNR), Structural Similarity Index Metric (SSIM), Universal Quality Index (UQI) and Image Enhancement Factor (IEF) were calculated. According to the calculation results, this study effectively improves the image quality, proves the effectiveness of the neural network, and has good clinical significance and broad application prospects.

Citation Download Citation

Ziming Yu, Kanggao Tang, and Xianlin Song "Denoising method for image quality improvement in photoacoustic microscopy using deep learning", Proc. SPIE 11875, Computational Optics 2021, 118750A (21 September 2021); https://doi.org/10.1117/12.2600759

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Denoising

Image quality

Photoacoustic spectroscopy

Photoacoustic microscopy

Image processing

Blood vessels

Interference (communication)

Show All Keywords

Keywords/Phrases

Search In:

Publication Years