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

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

Povzetek

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.

Ključne besede

medicinska fizika;hiperspektralno slikanje;nevronske mreže;medical physics;hyperspectral imaging;neural networks;

Podatki

Jezik:	Angleški jezik
Leto izida:	2025
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FMF - Fakulteta za matematiko in fiziko
UDK:	616-073
COBISS:	223069699
ISSN:	1083-3668
Št. ogledov:	24
Št. prenosov:	4
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	medicinska fizika;hiperspektralno slikanje;nevronske mreže;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 016004-1-016004-17
Letnik:	ǂVol. ǂ30
Zvezek:	ǂiss. ǂ1
Čas izdaje:	2025
DOI:	10.1117/1.JBO.30.1.016004
ID:	25739142