Fast and accurate skin parameter estimation from hyperspectral images using random Fourier features

Teo Manojlović; Tadej Tomanič; Ivan Štajduhar; Matija Milanič

doi:10.1117/12.3029449

20 June 2024 Fast and accurate skin parameter estimation from hyperspectral images using random Fourier features

Teo Manojlović, Tadej Tomanič, Ivan Štajduhar, Matija Milanič

Author Affiliations +

Proceedings Volume 13006, Biomedical Spectroscopy, Microscopy, and Imaging III; 130060N (2024) https://doi.org/10.1117/12.3029449
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Hyperspectral imaging (HSI) is a powerful tool for noninvasive assessment of skin properties, as it can capture the spectral signatures of different skin layers and components. However, HSI also requires efficient and accurate methods for estimating skin parameters, such as the thickness, scattering, and absorption coefficients of each skin layer, from the measured spectra. In recent years, much research has been done regarding the use of machine learning (ML) methods for reducing the time and computational cost required for estimating parameters, compared to classical methods, such as the inverse Monte Carlo (IMC) or the inverse adding-doubling (IAD) algorithm. In this study, we investigated the impact of using random Fourier features (RFF) with a simple linear regression model, as well as with an artificial neural network (ANN), to estimate parameter values directly from the spectra. We compared the proposed models with the ANN and a 1D convolutional neural network (CNN), both trained using the raw spectra as input. All models were trained on simulated data and evaluated on both simulated and in vivo measured spectra using mean absolute error (MAE). We found that even simple linear regression with RFFs performs comparably to the neural networks trained on raw spectra while having much lower training and inference time. The best results were attained with the RFF-based ANN, having an overall MAE of 0.0226, which is an improvement compared to the 1D-CNN, having an MAE of 0.0284.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Teo Manojlović, Tadej Tomanič, Ivan Štajduhar, and Matija Milanič "Fast and accurate skin parameter estimation from hyperspectral images using random Fourier features", Proc. SPIE 13006, Biomedical Spectroscopy, Microscopy, and Imaging III, 130060N (20 June 2024); https://doi.org/10.1117/12.3029449

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