2012
DOI: 10.1016/j.chemolab.2012.03.011
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Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Abstract: IntroductionWith the development of modern analytical technologies such as infrared (IR) and Raman spectroscopy the capabilities of both generating and collecting data has been tremendously increased. Time-resolved vibrational spectroscopy, microspectroscopy, and vibrational hyperspectral imaging for example are now routinely employed in many areas of industry, technology development and scientific research. The advancements in IR and Raman instrumentation has led to an explosive growth in stored or transient… Show more

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Cited by 285 publications
(235 citation statements)
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“…Instead of classical image analysis methods such as spatial filtering (e.g. sharpening, denoising), edge detection, segmentation and object recognition, HSI analysis methods currently rely predominantly on operations originally developed for point spectroscopy [20]. Vibrational hyperspectral imaging segmentation in biomedical applications is usually conducted via unsupervised spectral clustering [8,21], spectral unmixing [22][23][24], or supervised spectra classification [2,25].…”
Section: Introductionmentioning
confidence: 99%
“…Instead of classical image analysis methods such as spatial filtering (e.g. sharpening, denoising), edge detection, segmentation and object recognition, HSI analysis methods currently rely predominantly on operations originally developed for point spectroscopy [20]. Vibrational hyperspectral imaging segmentation in biomedical applications is usually conducted via unsupervised spectral clustering [8,21], spectral unmixing [22][23][24], or supervised spectra classification [2,25].…”
Section: Introductionmentioning
confidence: 99%
“…Employing the spectra for classification purposes requires some form of normalization that makes comparison between heterogeneous sets of samples more effectively (18,20). This is of high significance in our intended application in which different measurement systems were utilized.…”
Section: Data Collection and Data Analysis Proceduresmentioning
confidence: 99%
“…In the normalization step, different normalization methods (18) [including min-max, 1-norm, 2-norm and SNV (21)] were evaluated at different pre-processing stages i.e., on raw spectra, after resolution matching or after noise and background elimination.…”
Section: Data Collection and Data Analysis Proceduresmentioning
confidence: 99%
“…Such techniques include Min-max normalization, SNV normalization etc. These methods are applied individually to each spectrum, so these methods can be classified as 1-way methods [46]. Certain normalization methods considering more than one spectrum (say 'n' number of spectra) at a time while building the model can be classified as n-way methods.…”
Section: Normalizationmentioning
confidence: 99%
“…a) Both upper and lower thresholds can be applied on FTIR absorbance values of a specific vibration mode, for example the amide I region, which generally indicates inconsistent sample thickness regions [46,51]. For example, a low sample thickness is indicated by the presence of noise in the case of FTIR imaging data.…”
Section: Exclusion Of Low Snr Signalsmentioning
confidence: 99%