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 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The aim of this study was to proof applicability of hyperspectral imaging for the analysis and classification of human mucosal surfaces in vivo. The larynx as a prototypical anatomically well-defined surgical test area was analyzed by microlaryngoscopy with a polychromatic lightsource and a synchronous triggered monochromatic CCD-camera. Image stacks (5 benign, 7 malignant tumors) were analyzed by established software (principal component analysis PCA, hyperspectral classification, spectral profiles). Hyperspectral image datacubes were analyzed and classified by conventional software. In PCA, images at 590-680 nm loaded most onto the first PC which typically contained 95% of the total information. Hyperspectral classification clustered the data highlighting altered mucosa. The spectral profiles clearly differed between the different groups. Hyperspectral imaging can be applied to mucosal surfaces. This approach opens the way to analyze spectral characteristics of histologically different lesions in order to build up a spectral library and to allow non-touch optical biopsy.
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PMID:Hyperspectral imaging of mucosal surfaces in patients. 2223 73

Metamerism phenomenon is an important problem in spectral reflectance reconstruction and color reproduction. In this paper, a 3-primary color CCD camera is used to acquire spectral information in CIE standard illuminant D65 and a nonlinear composite model is established, including principal component analysis and neural network method (PCA-NET) to modify the Matrix R Method based on the Metameric Black theory. The standard Munsell color card is used in spectral reflectance reconstruction experiment and the results are evaluated and discussed. The experimental results verified that the PCA-NET algorithm can accurately fit the nonlinear relationship between the output signal of the camera and the principal component coefficients; and it can be used in the R matrix algorithm instead of the linear algorithm; the new method can serve as a promising technique for building a spectral image database whihc is better than the original Matrix R Method. In the fixed illumination environment, the mean RMS of the test set is 0.76 improved, and the mean STD of the test set is 0.85 improved, which can effectively improve the accuracy of spectral reflectance reconstruction. The modified matrix R method has the advantages of higher accuracy and easy implementation, and it can be used in the field of color reproduction and spectral reflectance reconstruction.
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PMID:Spectral Reflectance Reconstruction with Nonlinear Composite Model of the Metameric Black. 3014 48

Electron energy-loss spectroscopy (EELS) is a powerful tool for imaging chemical variations at the nanoscale. Here, we investigate a polymer/organic small molecule-blend used as absorber layer in an organic solar cell and employ EELS for distinguishing polymer donor and small molecule acceptor domains in the nanostructured blend based on elemental maps of light elements, such as nitrogen, sulfur or fluorine. Especially for beam sensitive samples, the electron dose needs to be limited, therefore optimized acquisition and data processing strategies are required. We compare data acquired on a post-column energy filter with a direct electron detection camera to data from a conventional CCD camera on the same filter and we investigate the impact of statistical data processing methods (principal components analysis, PCA) on acquired spectra and elemental maps extracted from spectrum images. Our work shows, that the quality of spectra on a direct electron detection camera is far superior to conventional CCD imaging, and thereby allows clear identification of ionization edges and the fine structure of these edges. For the quality of the elemental maps, the application of PCA is essential to allow a clear separation between the donor and acceptor phase in the bulk heterojunction absorber layer of a non-fullerene organic solar cell.
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PMID:Benefits of direct electron detection and PCA for EELS investigation of organic photovoltaics materials. 3320 62