Optimising and comparing source extraction tools using objective
  segmentation quality criteria
  With the growth of the scale, depth, and resolution of astronomical imaging
surveys, there is an increased need for highly accurate automated detection and
extraction of astronomical sources from images. This also means there is a need
for objective quality criteria, and automated methods to optimise parameter
settings for these software tools.
  We present a comparison of several tools which have been developed to perform
this task: namely SExtractor, ProFound, NoiseChisel, and MTObjects. In
particular, we focus on evaluating performance in situations which present
challenges for detection -- for example, faint and diffuse galaxies; extended
structures, such as streams; and objects close to bright sources. Furthermore,
we develop an automated method to optimise the parameters for the above tools.
  We present four different objective segmentation quality measures, based on
precision, recall, and a new measure for the correctly identified area of
sources. Bayesian optimisation is used to find optimal parameter settings for
each of the four tools on simulated data, for which a ground truth is known.
After training, the tools are tested on similar simulated data, to provide a
performance baseline. We then qualitatively assess tool performance on real
astronomical images from two different surveys.
  We determine that when area is disregarded, all four tools are capable of
broadly similar levels of detection completeness, while only NoiseChisel and
MTObjects are capable of locating the faint outskirts of objects. MTObjects
produces the highest scores on all tests on all four quality measures, whilst
SExtractor obtains the highest speeds. No tool has sufficient speed and
accuracy to be well-suited to large-scale automated segmentation in its current
form.
