A fast normalization and despeckled method for skin optical coherence tomography image via deep learning

Jarjish Rahaman; Brandon Lukas; Julia May; Carolina Puyana; Maria Tsoukas; Kamran Avanaki

doi:10.1117/12.2651211

14 March 2023 A fast normalization and despeckled method for skin optical coherence tomography image via deep learning

Jarjish Rahaman, Brandon Lukas, Julia May, Carolina Puyana, Maria Tsoukas, Kamran Avanaki

Proceedings Volume 12352, Photonics in Dermatology and Plastic Surgery 2023; 123520G (2023) https://doi.org/10.1117/12.2651211
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

Optical coherence tomography (OCT) is well-known for its high-resolution, non-invasive imaging modality with many medical uses, including skin imaging. Nevertheless, speckle noise limits the analytical capabilities of this imaging tool, causing deterioration in contrast and less exact detection of tissue microstructural heterogeneity. To address this issue, we proposed OCT despeckling approach by combing it with normalization to reduce the speckle noise more effectively. The proposed method contains multiple steps including phase correlation for alignment of misaligned frames, frame averaging which minimizes speckle noise, region-wise pixels normalization that helps to normalize intensity pixels, a modified BM3D filtering to suppress the white and speckle, and contrast enhancement to improve the contrast appropriately. To establish the approach, we applied 130 distinct B-scan skin OCT images and validate and evaluate the performance using qualitatively and quantitatively. Although the output obtained by the algorithm is promising, the method is time-consuming because of a series of steps. To reduce the time complexity, we also develop a supervised deep learning model by mapping between noisy-despeckled image pairs. The effectiveness and applicability of our DL approach were assessed using 130 skin OCT B-scans from various body areas taken from 45 healthy people between the ages of 20 and 60. With the support of the experimental results, we demonstrate that our DL model is capable to normalize and despeckling OCT images simultaneously.

Citation Download Citation

Jarjish Rahaman, Brandon Lukas, Julia May, Carolina Puyana, Maria Tsoukas, and Kamran Avanaki "A fast normalization and despeckled method for skin optical coherence tomography image via deep learning", Proc. SPIE 12352, Photonics in Dermatology and Plastic Surgery 2023, 123520G (14 March 2023); https://doi.org/10.1117/12.2651211

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