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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.

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.

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.

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.

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