  We present an automated approach to detect and extract information from the
astronomical datasets on the shapes of such objects as galaxies, star clusters
and, especially, elongated ones such as the gravitational lenses. First, the
Kolmogorov stochasticity parameter is used to retrieve the sub-regions that
worth further attention. Then we turn to image processing and machine learning
Principal Component Analysis algorithm to retrieve the sought objects and
reveal the information on their morphologies. We show the capability of our
automated method to identify distinct objects, including of and to classify
them based on the input parameters. A catalog of possible lensing objects is
retrieved as an output of the software, then their inspection is performed for
the candidates that survive the filters applied.
