on 5 separate acquisitions – the image on the top left shows the details for the middle part of the grain. This has significant implications for the dataset. Firstly, our number of grains will be wrong as we have multiple acquisitions for what we know to, in reality, be one grain. Secondly, our morphology measurements will be skewed towards smaller grain sizes and thirdly, the compositions that we calculate will be affected by being from multiple small areas instead of a larger area.
The bottom row of images shows the same data after AZtecFeature’s reconstruction algorithm has been run. Now, the bottom left image makes sense – we don’t see the checkerboard pattern anymore even though we are using the same colouring scheme. This is confirmed in the bottom middle image – the classification remains correct and, when we select the central grain we now get a morphology for it which is correct and a recalculated composition based upon the component parts.
So, as we’ve seen, magnification is a very important parameter for us to set correctly. By using both assisted magnification and reconstruction for larger features, AZtecFeature allows you to be confident that you are getting the right results from your feature analysis – regardless of feature size.
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Dr Matt Hiscock
Head of Product Science