A Multi-Spectral Image Database for In-Vivo Hand Perfusion Evaluation

Gutierrez-Navarro, Omar; Granados-Castro, Liliana; Mejía-Rodríguez, Aldo R.; Campos-Delgado, Daniel U.

doi:10.1109/access.2023.3305256

Cited by 3 publications

(1 citation statement)

References 51 publications

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“…Multi-spectral signals are the result of the interaction between electromagnetic energy and an object. Multi-spectral is also referred to as an image that uses several spectra [1], [2]. In multi-spectral signals, conditions are found which are called fluctuations which are caused by noise that interferes with the data.…”

Section: Introductionmentioning

confidence: 99%

Application of the Savitzky-Golay filter in multi-spectral signal processing

Syahrial,

Melinda,

Junidar

et al. 2024

Selco

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Multi-spectral signals are the result of the interaction between electromagnetic energy and the test material, which is then displayed by the signal fluctuation pattern of the test material. Signal fluctuations are inaccuracies in the peak amplitude of a signal caused by noise in the data. This fluctuation pattern reflects the properties of the test material, especially in this case H2O. To overcome this problem, it is necessary to use the right filter to smooth the signal and reduce the noise in the data so that the fluctuation pattern obtained is clearer and more accurate. This research involves the segmentation of HF fluctuation patterns, followed by the application of a Savitzky-Golay filter for signal smoothing. Signal quality is assessed objectively by calculating the Signal to Noise Ratio (SNR) and Mean Square Error (MSE). The research results show that the Savitzky-Golay filter succeeded in reducing noise and producing clearer fluctuation patterns. The SNR value varies, with the largest value reaching 16.6146 dB, and the smallest value being 3.0171 dB. This research contributes to a new method, namely the Savitzky-Golay adaptive filter, to identify multi-spectral signal fluctuation patterns more effectively, thereby enabling more accurate identification of fluctuation patterns. Apart from that, this research also provides insight into the characteristics of H2O which can be identified through fluctuation patterns, especially in certain segments with high amplitude. This method has potential for applications in various fields, especially in precise multi-spectral signal analysis.

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Section: Introductionmentioning

confidence: 99%

Application of the Savitzky-Golay filter in multi-spectral signal processing

Syahrial,

Melinda,

Junidar

et al. 2024

Selco

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Camera-based physiological measurement: Recent advances and future prospects

Wang,

Shan,

Liu

et al. 2024

Neurocomputing

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Contactless Skin Blood Perfusion Imaging via Multispectral Information, Spectral Unmixing and Multivariable Regression

Granados-Castro,

Gutierrez-Navarro,

Mejia-Rodriguez

et al. 2024

IEEE Open J. Signal Process.

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Noninvasive methods for assessing in-vivo skin blood perfusion parameters, such as hemoglobin oxygenation, are crucial for diagnosing and monitoring microvascular diseases. This approach is particularly beneficial for patients with compromised skin, where standard contact-based clinical devices are inappropriate. For this goal, we propose the analysis of multimodal data from an occlusion protocol applied to 18 healthy participants, which includes multispectral imaging of the whole hand and reference photoplethysmography information from the thumb. Multispectral data analysis was conducted using two different blind linear unmixing methods: principal component analysis (PCA), and extended blind endmember and abundance extraction (EBEAE). Perfusion maps for oxygenated and deoxygenated hemoglobin changes in the hand were generated using linear multivariable regression models based on the unmixing methods. Our results showed high accuracy, with R 2 -adjusted values, up to 0.90±0.08. Further analysis revealed that using more than four characteristic components during spectral unmixing did not improve the fit of the model. Bhattacharyya distance results showed that the fitted models with EBEAE were more sensitive to hemoglobin changes during occlusion stages, up to four times higher than PCA. Our study concludes that multispectral imaging with EBEAE is effective in quantifying changes in oxygenated hemoglobin levels, especially when using 3 to 4 characteristic components. Our proposed method holds promise for the noninvasive diagnosis and monitoring of superficial microvascular alterations across extensive anatomical regions.INDEX TERMS skin blood perfusion, hemoglobin, multispectral imaging, photoplethysmography, spectral unmixing.

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A Multi-Spectral Image Database for In-Vivo Hand Perfusion Evaluation

Cited by 3 publications

References 51 publications

Application of the Savitzky-Golay filter in multi-spectral signal processing

Application of the Savitzky-Golay filter in multi-spectral signal processing

Camera-based physiological measurement: Recent advances and future prospects

Contactless Skin Blood Perfusion Imaging via Multispectral Information, Spectral Unmixing and Multivariable Regression

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