Denoising of single scan Raman spectroscopy signals

Hernández, Luis Héctor Quintero; Matthäus, Christian; Hunt, Shawn; Diem, Max

doi:10.1117/12.842851

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…However, the signal associated with inelastically scattered light is weak; it is several orders of magnitude smaller when compared to elastic scattering and other sources of background light such as intrinsic tissue fluorescence. Thus, one of the limiting factors for biological applications is stochastic noise, which can overwhelm the Raman signal that is tissue-specific [19][20][21][22][23]. Consequently, the inelastic scattering signal-to-noise ratio (SNR) is a very important concept in Raman spectroscopy, to determine the origin of the signal and evaluate the quality of the measurements.…”

Section: Signal To Noise Ratiomentioning

confidence: 99%

Characterization of a Raman spectroscopy probe system for intraoperative brain tissue classification

Desroches

Jermyn

Mok

et al. 2015

Biomed. Opt. Express

125

102

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Abstract:A detailed characterization study is presented of a Raman spectroscopy system designed to maximize the volume of resected cancer tissue in glioma surgery based on in vivo molecular tissue characterization. It consists of a hand-held probe system measuring spectrally resolved inelastically scattered light interacting with tissue, designed and optimized for in vivo measurements. Factors such as linearity of the signal with integration time and laser power, and their impact on signal to noise ratio, are studied leading to optimal data acquisition parameters. The impact of ambient light sources in the operating room is assessed and recommendations made for optimal operating conditions. In vivo Raman spectra of normal brain, cancer and necrotic tissue were measured in 10 patients, demonstrating that real-time inelastic scattering measurements can distinguish necrosis from vital tissue (including tumor and normal brain tissue) with an accuracy of 87%, a sensitivity of 84% and a specificity of 89%.

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Section: Signal To Noise Ratiomentioning

confidence: 99%

Characterization of a Raman spectroscopy probe system for intraoperative brain tissue classification

Desroches

Jermyn

Mok

et al. 2015

Biomed. Opt. Express

125

102

View full text Add to dashboard Cite

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“…The spectrum was submitted to the automatic baseline correction process before it was smoothed using the smoothing package within the FTIR software. According to Quintero et al, 32 during the acquisition process, noise may affect the spectrum more than once. Thus, smoothing was required after the acquisition process to improve the appearance of spectrum.…”

Section: Spectrum Acquisitionmentioning

confidence: 99%

Automated cervical precancerous cells screening system based on Fourier transform infrared spectroscopy features

Jusman

Isa

et al. 2016

J. Biomed. Opt

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Fourier transform infrared (FTIR) spectroscopy technique can detect the abnormality of a cervical cell that occurs before the morphological change could be observed under the light microscope as employed in conventional techniques. This paper presents developed features extraction for an automated screening system for cervical precancerous cell based on the FTIR spectroscopy as a second opinion to pathologists. The automated system generally consists of the developed features extraction and classification stages. Signal processing techniques are used in the features extraction stage. Then, discriminant analysis and principal component analysis are employed to select dominant features for the classification process. The datasets of the cervical precancerous cells obtained from the feature selection process are classified using a hybrid multilayered perceptron network. The proposed system achieved 92% accuracy.

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“…Here, the noisy raw spectral data are transformed into a wavelet domain by decomposing it into a set of orthonormal wavelet basis functions. The major signal trends of the spectrum are assignable to large wavelet coefficients, whereas the noise is assignable to only small coefficients . Hereupon, the noise is suppressed by thresholding the wavelet coefficients.…”

Section: Introductionmentioning

confidence: 99%

Vector casting for noise reduction

Gebrekidan

Knipfer

Braeuer

2020

J Raman Spectroscopy

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We report a new method for the reduction of noise from spectra. This method is based on casting vectors from one data point to the following data points of the noisy spectrum. The noise‐reduced spectrum is computed from the casted vectors within a margin that is identified by an envelope‐finder algorithm. We compared here the presented method with the Savitzky–Golay and the wavelet transform approaches for noise reduction using simulated Raman spectra of various signal‐to‐noise ratios between 1 and 25 dB and experimentally acquired Raman spectra. The method presented here performs well compared with the Savitzky–Golay and the wavelets‐based denoising method, especially at small signal‐to‐noise ratios and furthermore relies on a minimum of human input requirements.

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Denoising of single scan Raman spectroscopy signals

Cited by 8 publications

References 8 publications

Characterization of a Raman spectroscopy probe system for intraoperative brain tissue classification

Characterization of a Raman spectroscopy probe system for intraoperative brain tissue classification

Automated cervical precancerous cells screening system based on Fourier transform infrared spectroscopy features

Vector casting for noise reduction

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