  Recently, Radon transformation has been used to generate barcodes for tagging
medical images. The under-sampled image is projected in certain directions, and
each projection is binarized using a local threshold. The concatenation of the
thresholded projections creates a barcode that can be used for tagging or
annotating medical images. A small number of equidistant projections, e.g., 4
or 8, is generally used to generate short barcodes. However, due to the diverse
nature of digital images, and since we are only working with a small number of
projections (to keep the barcode short), taking equidistant projections may not
be the best course of action. In this paper, we proposed to find $n$ optimal
projections, whereas $n\!<\!180$, in order to increase the expressiveness of
Radon barcodes. We show examples for the exhaustive search for the simple case
when we attempt to find 4 best projections out of 16 equidistant projections
and compare it with the evolutionary approach in order to establish the benefit
of the latter when operating on a small population size as in the case of
micro-DE. We randomly selected 10 different classes from IRMA dataset (14,400
x-ray images in 58 classes) and further randomly selected 5 images per class
for our tests.
