Preprocess images: Difference between revisions

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* '''Clean''' from false particles (no images are discarded during refinement).  
* '''Clean''' from false particles (no images are discarded during refinement).  
** Xmipp implements an image sorting utility called <code>xmipp_sort_by_statistics</code> that is ''very'' handy in the cleaning of a data set.  
** Xmipp implements an image sorting utility called <code>xmipp_sort_by_statistics</code> that is ''very'' handy in the cleaning of a data set.  
* Unmasked (masking is performed internally)
* '''Unmasked''' (masking is performed internally)
* '''Non-interpolated''' (prevent any prior rotations/translations: use the originally scanned pixel values)
* '''Non-interpolated''' (prevent any prior rotations/translations: use the originally scanned pixel values)
** If downscaling is necessary because of memory issues: use a window-operation in Fourier-space, not a convolution (e.g. with rectangle/B-spline).  
** If downscaling is necessary because of memory issues: use a window-operation in Fourier-space, not a convolution (e.g. with rectangle/B-spline).  

Revision as of 20:51, 27 September 2011

RELION will work best if your data set is:

  • Clean from false particles (no images are discarded during refinement).
    • Xmipp implements an image sorting utility called xmipp_sort_by_statistics that is very handy in the cleaning of a data set.
  • Unmasked (masking is performed internally)
  • Non-interpolated (prevent any prior rotations/translations: use the originally scanned pixel values)
    • If downscaling is necessary because of memory issues: use a window-operation in Fourier-space, not a convolution (e.g. with rectangle/B-spline).
    • Xmipp implements the Fourier-space downscaling in the xmipp_scale program with the -fourier option.
  • Uncorrected for CTF (this is done internally)
    • If your data have previously been phase-flipped, that's OK: just set the corresponding option
    • If your data have previously been pre-Wiener filtered, that's a VERY bad thing to do in general: go back to the original data.
  • Normalised (the exact procedure does not matter too much, as errors in the normalisation are corrected internally)

And then, just like with any other refinement program, you might save yourself lots of trouble if your data set has:

  • high signal-to-noise ratios (take great care in sample preparation and data collection)