Abstract
Hyperspectral images of tissue contain extensive and complex information relevant for clinical applications. In this work, wavelet decomposition is explored for feature extraction from such data. Wavelet methods are simple and computationally effective, and can be implemented in real-time. The aim of this study was to correlate results from wavelet decomposition in the spectral domain with physical parameters (tissue oxygenation, blood and melanin content). Wavelet decomposition was tested on Monte Carlo simulations, measurements of a tissue phantom and hyperspectral data from a human volunteer during an occlusion experiment. Reflectance spectra were decomposed, and the coefficients were correlated to tissue parameters. This approach was used to identify wavelet components that can be utilized to map levels of blood, melanin and oxygen saturation. The results show a significant correlation (p <0.02) between the chosen tissue parameters and the selected wavelet components. The tissue parameters could be mapped using a subset of the calculated components due to redundancy in spectral information. Vessel structures are well visualized. Wavelet analysis appears as a promising tool for extraction of spectral features in skin. Future studies will aim at developing quantitative mapping of optical properties based on wavelet decomposition.
Highlights
Hyperspectral imaging, which combines spectroscopy with optical imaging, has been proposed and employed as a diagnostic tool for clinical examination of tissue [1,2,3]
The Discrete Wavelet Transform (DWT) algorithm rearranges the information in the hyperspectral images and emphasizes features of the analyzed spectra appearing at different scales and wavelengths
Simulations have been used to investigate the link between tissue properties and DWT coefficients
Summary
Hyperspectral imaging, which combines spectroscopy with optical imaging, has been proposed and employed as a diagnostic tool for clinical examination of tissue [1,2,3]. Hyperspectral imaging is essentially spatially resolved diffuse reflectance spectroscopy. Diffuse reflectance spectra in the visible to near infra-red region contain a vast amount of information, originating from the absorption and scattering properties of the tissue constituents. Such information may be valuable in characterization and diagnosis of tissue in applications such as e.g. monitoring of wound healing, cutaneous burns and radio therapy induced skin changes. Hyperspectral data and reflectance spectra are currently processed by simple wavelength ratios, statistical image analysis or by using analytical or numerical methods such as diffusion theory or Monte Carlo techniques
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