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Hello Alan<br class="">
<br class="">
From what you say, it sounds as if there may be some parallels between your data and the Fluorescence Lifetime Imaging (FLIM) data that I’ve been working with.<br class="">
In the same way the basic unit of FLIM data is a datacube , in this case x,y and time as opposed to x,y and, I assume, wavelength for your data.<br class="">
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Both OMERO and the OME-tiff standard are designed for up to 5D data: the 5 dimensions being x,y,z, Channels and Time.
<div class="">So I’m confident that your data can easily stored in an OMERO server using the C(hannels) dimension giving you all the advantages </div>
<div class="">of that.</div>
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<div class="">A sample of our data can be viewed at <a href="https://cisbic.bioinformatics.ic.ac.uk/omero/webclient/?show=image-110694" class="">https://cisbic.bioinformatics.ic.ac.uk/omero/webclient/?show=image-110694</a></div>
<div class="">By double-clicking and using the slider you can move through the time dimension and data with many Channels would display in pretty much the same way</div>
<div class="">with the provided clients.</div>
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<div class="">There are two issues with viewing the data that are unique to FLIM </div>
<div class="">1) the raw images typically contain only a few photons at each time point </div>
<div class="">2) we are interested in the rate of decay across time rather than the intensity.</div>
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<div class="">We have therefore developed an OMERO client called FLIMfit that allows this data to be viewed in a more convenient manner and also processed.</div>
<div class="">Opening the same image with FLIMfit shows a total intensity image on the left and the data at all time points for the chosen pixel on the right (see screenshot).</div>
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<div class="">From your comments my suspicion is that a similar approach would work well for your data.</div>
<div class="">Please feel free to contact me if you need further information or indeed a demo given how close we are.</div>
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<div style="margin: 0px; font-family: Courier;" class="">Kind Regards</div>
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<div style="margin: 0px; font-family: Courier;" class="">Dr Ian Munro</div>
<div style="margin: 0px; font-family: Courier;" class="">Photonics Group</div>
<div style="margin: 0px; font-family: Courier;" class="">Imperial College, London.</div>
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<div style="margin: 0px; font-family: Courier;" class="">Kind Regards</div>
<div style="margin: 0px; font-family: Courier; min-height: 14px;" class=""><br class="">
</div>
<div style="margin: 0px; font-family: Courier;" class="">Dr Ian Munro</div>
<div style="margin: 0px; font-family: Courier;" class="">Photonics Group</div>
<div style="margin: 0px; font-family: Courier;" class="">Imperial College, London.</div>
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<blockquote type="cite" class="">
<div class="">On 3 Nov 2015, at 14:19, Alan Race <<a href="mailto:alan.race@npl.co.uk" class="">alan.race@npl.co.uk</a>> wrote:</div>
<br class="Apple-interchange-newline">
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<div class="">Hello,<br class="">
I am currently involved in project that is evaluating various tools for providing a single platform for visualising and processing multi-modality data and was wondering how easy it would be to integrate new types of data into OME. Specifically I am primarily
interested in spectral imaging techniques (e.g. mass spectrometry imaging, Raman) where every "pixel" has an associated spectrum and chemical images are generated by integrating over a peak in the spectrum and plotting the resulting sum at the pixel location.<br class="">
<br class="">
The data can be thought of as a "datacube" or image stack, typically consisting of thousands of images. What underlying structures do you use to handle images? Are there already capabilities to cope with such data?<br class="">
<br class="">
Thank you for your help,<br class="">
<br class="">
Alan<br class="">
<br class="">
Alan Race<br class="">
Research Scientist<br class="">
National Physical Laboratory<br class="">
Hampton Road | Teddington | TW11 0LW | UK<br class="">
T: +44 (0) 20 8943 6649<br class="">
<br class="">
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