Biomedical Engineering Reference
In-Depth Information
When imaging at industrial scales, automation is crucial so the macro
written within ImageJ was fully automatic and only had to be directed
towards the folder of starting images. Using the control structures of for
loops, if and else statements and arrays together with the ImageJ
commands and macro functions, a creative workfl ow was built to fully
automate image analysis tasks. Output was in the form of a comma
delimited text fi le, which can be further manipulated in applications such
as Microsoft Excel. The output of false colour images of the seeds showed
exactly which objects were classifi ed as seeds, with a white line drawn on
them that represented the feret distance (also known as the maximum
calliper), which was the longest dimension of the seed.
The next example is a type of analysis which is challenging to the
human observer, and measures both colour and sizes. Plant leaves can
take on a variety of hues and the goal was to classify them into different
categories. Furthermore, the plant spread, represented by measuring the
convex hull, was also needed. The starting image and the processed and
analysed image are shown in Figure 5.8.
Plant phenotyping to non-subjectively quantify the
areas of different colour classifi cations. The starting
image is shown on the left-hand side and the
segmented plant in classifi ed false colour is shown on
the right-hand side (converted here to a greyscale
image). Note that the convex hull area to measure
plant spread is shown surrounding the plant. False
colour images are as important as the actual text
results as they demonstrate to the user that the
parameters have been measured correctly
Figure 5.8
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