Robust estimation of skin physiological parameters from hyperspectral images using Bayesian neural networks

Manojlović, Teo (avtor), Tomanič, Tadej (avtor), Štajduhar, Ivan (avtor), Milanič, Matija (avtor)

Significance: Machine learning models for the direct extraction of tissue parameters from hyperspectral images have been extensively researched recently, as they represent a faster alternative to the well-known iterative methods such as inverse Monte Carlo and inverse adding-doubling (IAD). Aim: We aim to develop a Bayesian neural network model for robust prediction of physiological parameters from hyperspectral images. Approach: We propose a two-component system for extracting physiological parameters from hyperspectral images. First, our system models the relationship between the measured spectra and the tissue parameters as a distribution rather than a point estimate and is thus able to generate multiple possible solutions. Second, the proposed tissue parameters are then refined using the neural network that approximates the biological tissue model. Results: The proposed model was tested on simulated and in vivo data. It outperformed current models with an overall mean absolute error of 0.0141 and can be used as a faster alternative to the IAD algorithm. Conclusions: Results suggest that Bayesian neural networks coupled with the approximation of a biological tissue model can be used to reliably and accurately extract tissue properties from hyperspectral images on the fly.

• Jezik: Angleški jezik
• Ključne besede: medical physics, hyperspectral imaging, neural networks
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FMF - Fakulteta za matematiko in fiziko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: Str. 016004-1-016004-17
• Številčenje: Vol. 30, iss. 1
• PID: 20.500.12556/RUL-166647
• UDK: 616-073
• ISSN pri članku: 1083-3668
• DOI: 10.1117/1.JBO.30.1.016004
• COBISS.SI-ID: 223069699
• Datum objave v RUL: 20.01.2025

Gradivo je del revije

• Naslov: Journal of biomedical optics
• Skrajšan naslov: J. biomed. opt.
• Založnik: SPIE--the International Society for Optical Engineering, International Biomedical Optics Society
• ISSN: 1083-3668
• COBISS.SI-ID: 18188071

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: medicinska fizika, hiperspektralno slikanje, nevronske mreže