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Work Group Statistical Modelling and Image Analysis

Work Field

The work group Statistical Modelling and Image Analysis is engaged in the adequate analysis of spectrometric, spectroscopic and image data. Furthermore, methods are being developed for the quantitative correlation of different measurement methods. To support these analytical methods simulations of specimen properties and the measuring method itself are performed. More information about these different working fields can be found in the next sections.

Publications [1, 2, 3, 4]

Spectral Data Analysis

One major field of research, which is currently tackled by the researchers of the junior group, is spectral data analysis. Within this research topic optimal procedures and methods to correct and analyze different types of spectral data are been developed. Our major expertise is for the analysis of Raman spectra, but we also investigate the chemometric procedures to analyze other kinds of spectral data, like NIR spectra and MALDI-spectra. The performed studies aim to determine, which analysis and correction procedures are optimal suited and should be combined for an optimal and fully automatic data pipeline. In order to do so, we investigate the whole data pipeline and all its procedures.

Publications [5, 6, 7, 8]

Image Analysis

Another field of the junior group’s work is devoted to the analysis of multimodal images, which are composed of CARS, TPEF and SHG images, and other kind of image data. In all our image related studies, we try to translate  physical measurements into bio-medical information. In order to achieve this aim, we construct a data pipeline, which is composed of experimental design, certain correction procedures, feature extraction and model construction. We perform investigations on all of these procedures, in order to receive an optimal data pipeline, which delivers reliable and robust results.

Publications [9, 10, 11]

Correlation of Methods

The third major field of work of the junior group is the correlation and combination of data derived from different measurement modalities. In this part, we try to fuse data from different measurement modalities and use the fused data together. The idea behind this data fusion is the use of complementary information (from different measurement modalities) together in order to receive a more comprehensive understanding of the sample. Another aim of the investigated data fusion is the combination of one technique with a reference technique and the quantification of the difference. In order to achieve these two goals, we investigate data fusion strategies and apply them to the combinations of various techniques.

[1]    T.W. Bocklitz, T. Dörfer, R. Heinke, M. Schmitt, and J. Popp. Spectrometer calibration protocol for Raman spectra recorded with different excitation wavelengths. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 05:544–549, 2015.
[2]    Shuxia Guo, Thomas Bocklitz, and Jürgen Popp. Optimization of Raman-spectrum baseline correction in biological application. Analyst, in print:–, 2016.
[3]    Shuxia Guo, Ralf Heinke, Stephan Stöckel, Petra Rösch, Thomas Bocklitz, and Jürgen Popp. Towards an improvement of model transferability for raman spectroscopy in biological applications. Vibrational Spectrocopy, akzeptiert, 2016.
[4]    Oleg Ryabchykov, Thomas Bocklitz, Anuradha Ramoji, Ute Neugebauer, Martin Foester, Claus Kroegel, Michael Bauer, Michael Kiehntopf, and Juergen Popp. Automatization of spike correction in Raman spectra of biological samples. Chemom. Intell. Lab. Syst., 155:1–6, 2016.
[5]    Fisseha Bekele Legesse, Olga Chernavskaia, Sandro Heuke, Thomas Bocklitz, Tobias Meyer, Jürgen Popp, and Rainer Heintzmann. Seamless stitching of tile scan microscope images. J. Microsc., 258:223–230, 2015.
[6]    Thomas Bocklitz, Firas Subhi Salah, Nadine Vogler, Sandro Heuke, Olga Chernavskaia, Carsten Schmidt, Maximilian Waldner, Florian R. Greten, Rolf Bräuer, Michael Schmitt, Andreas Stallmach, Iver Petersen, and Juergen Popp. Combining CARS/TPEF/SHG multimodal imaging and Raman-spectroscopy as a fast and precise non-invasive pathological screening tool. BMC Cancer, akzeptiert, 2016.
[7]    Olga Chernavskaia, Sandro Heuke, Michael Vieth, Oliver Friedrich, Sebastian Schürmann, Raja Atreya, Andreas Stallmach, Markus F. Neurath, Maximilian Waldner, Iver Petersen, Michael Schmitt, Thomas Bocklitz, and Jürgen Popp. Beyond endoscopic assessment in inflammatory bowel disease: real-time histology of disease activity by non-linear multimodal imaging. Scientific Reports, 6:29239, 2016.
[8]    Sandro Heuke, Olga Chernavskaia, Thomas Bocklitz, Fisseha Bekele Legesse, Tobias Meyer, Denis Akimov, Olaf Dirsch, Günther Ernst, Ferdinand von Eggeling, Iver Petersen, Orlando Guntinas-Lichius, Michael Schmitt, and Jürgen Popp. Multimodal nonlinear microscopic investigations on head and neck squamous cell carcinoma – toward surgery assisting frozen section analysis. Head & Neck, online, 2016.
[9]    Thomas Bocklitz, Katharina Bräutigam, Annett Urbanek, Franziska Hoffmann, Ferdinand von Eggeling, G ünther Ernst, Michael Schmitt, Ulrich Schubert, Orlando Guntinas-Lichius, and Jürgen Popp. Novel workflow for combining Raman-spectroscopy and MALDI-MSIs for tissue based studies. Analytical and Bioanalytical Chemistry, 407(26):7865–7873, 2015.
[10]    Sebastian Dochow, Dinglong Ma, Ines Latka, Thomas Bocklitz, Brad Hartl, Julien Bec, Hussain Fatakdawala, Eric Marple, Kirk Urmey, Sebastian Wachsmann-Hogiu, Michael Schmitt, Laura Marcu, and Jürgen Popp. Combined fiber probe for fluorescence lifetime and Raman spectroscopy. Anal. Bioanal. Chem., 407:8291–301, 2015.
[11]    R. Geitner, J. Kötteritzsch, M. Siegmann, T. W. Bocklitz, M. D. Hager, U. S. Schubert, B. Dietzek S. Gräfe and, M. Schmitt, and J. Popp. Two-dimensional Raman correlation spectroscopy reveals molecular structural changes during temperature-induced self-healing in polymers based on the Diels-Alder reaction. Phys. Chem. Chem. Phys., 17:22587–95, 2015.

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