O. Piot - Academia.edu (original) (raw)
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Papers by O. Piot
Journal of Biophotonics, 2008
IEEE Transactions on Biomedical Engineering, 2000
Raman spectra are classically modeled as a linear mixing of spectra of molecular constituents of ... more Raman spectra are classically modeled as a linear mixing of spectra of molecular constituents of the analyzed sample. Source separation methods are thus well suited to estimate these constituent spectra. However, physical distortions due to the instrumentation and biological nature of samples add nonlinearities to the Raman spectra model. These distortions are dark current, detector and optic responses, fluorescence background, and peak misalignment and peak width heterogeneity. The source separation results are thus deteriorated by these effects. We propose to develop specific preprocessing steps to correct these distortions and to retrieve a linear model. The benefits brought by these steps are studied by the application of two different source separation methods named joint approximate diagonalization of eigenmatrices and maximum likelihood positive source separation after the application of each step on a dataset acquired on a paraffin-embedded human skin biopsy. The efficacy of these methods to separate Raman spectra is also discussed.
Mid-IR spectral imaging is an efficient method to analyze biological samples. Several research st... more Mid-IR spectral imaging is an efficient method to analyze biological samples. Several research studies showed its potential to diagnose cancerous tissues. However, some limitations appear when formalin-fixed paraffin-embedded tissues are studied due to the intense IR contribution of paraffin, unless to perform a time-consuming and aggressive chemical dewaxing. We propose in this paper to analyze the efficiency of two digital
Journal of Biophotonics, 2008
IEEE Transactions on Biomedical Engineering, 2000
Raman spectra are classically modeled as a linear mixing of spectra of molecular constituents of ... more Raman spectra are classically modeled as a linear mixing of spectra of molecular constituents of the analyzed sample. Source separation methods are thus well suited to estimate these constituent spectra. However, physical distortions due to the instrumentation and biological nature of samples add nonlinearities to the Raman spectra model. These distortions are dark current, detector and optic responses, fluorescence background, and peak misalignment and peak width heterogeneity. The source separation results are thus deteriorated by these effects. We propose to develop specific preprocessing steps to correct these distortions and to retrieve a linear model. The benefits brought by these steps are studied by the application of two different source separation methods named joint approximate diagonalization of eigenmatrices and maximum likelihood positive source separation after the application of each step on a dataset acquired on a paraffin-embedded human skin biopsy. The efficacy of these methods to separate Raman spectra is also discussed.
Mid-IR spectral imaging is an efficient method to analyze biological samples. Several research st... more Mid-IR spectral imaging is an efficient method to analyze biological samples. Several research studies showed its potential to diagnose cancerous tissues. However, some limitations appear when formalin-fixed paraffin-embedded tissues are studied due to the intense IR contribution of paraffin, unless to perform a time-consuming and aggressive chemical dewaxing. We propose in this paper to analyze the efficiency of two digital