OpenCL framework for fast estimation of optical properties from spatial frequency domain images

Peter Naglič; Yevhen Zelinskyi; Boštjan Likar; Franjo Pernuš; Miran Bürmen

doi:10.1117/12.2509986

4 March 2019 OpenCL framework for fast estimation of optical properties from spatial frequency domain images

Peter Naglič, Yevhen Zelinskyi, Boštjan Likar, Franjo Pernuš, Miran Bürmen

Proceedings Volume 10889, High-Speed Biomedical Imaging and Spectroscopy IV; 1088919 (2019) https://doi.org/10.1117/12.2509986
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

To overcome the drawbacks of the commonly used lookup table inverse models, we propose a novel custom OpenCL™- accelerated artificial neural network inverse model for spatial frequency domain imaging (https://bitbucket.org/xopto /rftroop). Utilizing a mid-range graphics processing unit, the proposed inverse model can estimate high-definition (1920 × 1080) maps of the absorption and reduced scattering coefficients and two scattering phase function related quantifiers at a rate of more than 50 frames per second. We show that the artificial neural network inverse model can be seamlessly extended to estimate multiple optical properties independently, thus providing a versatile framework that allows introduction of new quantifiers.

Citation Download Citation

Peter Naglič, Yevhen Zelinskyi, Boštjan Likar, Franjo Pernuš, and Miran Bürmen "OpenCL framework for fast estimation of optical properties from spatial frequency domain images", Proc. SPIE 10889, High-Speed Biomedical Imaging and Spectroscopy IV, 1088919 (4 March 2019); https://doi.org/10.1117/12.2509986

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