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Management of your image capture process is critical for the success of 2D-electrophoresis gel analysis. Ensuring the consistent quality of scanned images is most problematic for large projects involving multiple users at multiple sites. This lack of control will impact on the subsequent detection and quantitation of the images, thus reducing the statistical power of the resulting data analysis.
Most image problems are well known and their causes well understood; for example: incorrect image types, color or compressed images, low spatial resolution, varying bit depth, large and variable image file sizes, low or inconsistent use of dynamic ranges, large border variation, uncontrolled image noise, using the same image more than once, and multiple image orientations.
To help the investigator reduce the occurrence of such problems, we have investigated methods of automatic detection, and propose a standardized way of assessing and rejecting images to avoid subjective decisions that lead to bias. We will show examples of how this process can be used to improve the performance of your capture workflow and thus ensure that you get the best out of your 2D image data.