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Vogler, Nadine ; Bocklitz, Thomas ; Mariani, Melissa ; Deckert, Volker ; Markova, Aneta ; Schelkens, Peter ; Rösch, Petra ; Akimov, Denis ; Dietzek, Benjamin ; Popp, Jürgen:
Separation of CARS image contributions with a Gaussian Mixture Model.
In: Journal of the Optical Society of America A.
Bd. 27
(2010)
Heft 6
.
- S. 1361-1371.
ISSN 1520-8532
DOI: https://doi.org/10.1364/JOSAA.27.001361
Abstract
Coherent anti-Stokes Raman scattering (CARS) gained a lot of importance in chemical imaging. This is due to the fast image acquisition time, the high spatial resolution, the non-invasiveness, and the molecular sensitivity of this method. By using the single-line CARS in contrast to the multiplex CARS, different signal contributions stemming from resonant and non-resonant light–matter interactions are indistinguishable. Here a numerical method is presented in order to extract more information from univariate CARS images: vibrational composition, morphological information, and contributions from index-of-refraction steps can be separated from single-line CARS images. The image processing algorithm is based on the physical properties of CARS process as reflected in the shape of the intensity histogram of univariate CARS images. Because of this the comparability of individual CARS images recorded with different experimental parameters is achieved. The latter is important for a quantitative evaluation of CARS images.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Institutionen der Universität: | Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 500 Naturwissenschaften und Mathematik > 530 Physik |
| Eingestellt am: | 12 Mai 2023 10:08 |
| Letzte Änderung: | 12 Mai 2023 10:08 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76377 |