Imaging global

To evaluate the image quality of the processing system, one can determine classical parameters like spatial resolution, contrast resolution, dynamic range, local and global distortion. Guidelines for film digitization procedures have been well described now. Furthermore, a physical standard film for both equipment assessment and digitization calibration and control, will be available in a next future (4). 

Figure 2 Data cube generation in mapping and imaging. The four-dimensional hyperspectral data cube contains the full spectral information, absorbance vs. wavenumber (v, cm ), for each x,y pixel from the imaged area, as is shown above. A horizontal slice through that cube contains a chemical image (e.g., band intensity at selected v for each x,y pixel of the image) as is shown below. The latter result could be obtained by Global Imaging (in which only the intensity distribution of a certain band over the imaged area would be recorded).

Global Raman imaging can be a fast and simple technique, providing high lateral spatial resolution (down to the diffraction limit corresponding with the excitation laser wavelength) images of the sample of interest. There are several techniques available. 

Figure 3 Global Raman imaging Experimental setup and example image of a silicon wafer with letter E printed on it. Image taken at 520 cm 1 (Silicon Raman mode).

Figure 10 Global Raman image of PTFE distribution in PA-matrix image taken at 731 cm- Left white light micrograph center Raman image right the same image after image enhancement and edge detection.

The advantages of imaging (using multichannel detectors or global imaging methods) over conventional mapping experiments are obvious, whenever multicomponent samples have to be investigated ... 

There are two possible approaches. In one approach we in effect build the execution sequence tree for P. We start with node (1,0) labelled START. A node (k,r) will be at level r of the new tree-like structure and be labelled with the instruction named by k. Suppose statement k in P is connected by an arrow (with or without a label) to statement p in P and that we have constructed node (k,r) in P to date. If (k,r) has no ancestor of the form (p,r ), r < r, place node (p,r+l) labelled by statement p on the tree, with an arrow from (k,r) to (p,r+l) which contains any label on the arrow from k to p. If there is already an ancestor (p,rT), r < r, of (k,r) on the tree, then do not create (p,r+l) but instead add an arrow from (k,r) back to (p,r ) containing any label also on the arrow from k to p. If P has N statements, this process must terminate in a scheme P with at most N levels. Clearly P is tree-like and is strongly equivalent to P. This transformation is global and structure preserving. In fact P is a strong homomorphic image of P under the homomorphism h taking each (k,r>) back into k. ... 

Verveer, P. J. and Bastiaens, P. I. H. (2003). Evaluation of global analysis algorithms for single frequency fluorescence lifetime imaging microscopy data. J. Microsc. 209, 1-7. 

Also global fitting techniques, where the space invariance (or any other invariance property) of one or more fitting parameters is exploited, have been successfully used to analyze fluorescence lifetime images [45, 46]. When applicable, global analysis techniques provide more homogeneous SNRs and reduce the number of fitted parameters. 

Pelet, S., Previte, M. J., Laiho, L. H. and So, P. T. (2004). A fast global fitting algorithm for fluorescence lifetime imaging microscopy based on image segmentation. Biophys. J. 87, 2807-17. 

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