  We present an innovative method called FilExSeC (Filaments Extraction,
Selection and Classification), a data mining tool developed to investigate the
possibility to refine and optimize the shape reconstruction of filamentary
structures detected with a consolidated method based on the flux derivative
analysis, through the column-density maps computed from Herschel infrared
Galactic Plane Survey (Hi-GAL) observations of the Galactic plane. The present
methodology is based on a feature extraction module followed by a machine
learning model (Random Forest) dedicated to select features and to classify the
pixels of the input images. From tests on both simulations and real
observations the method appears reliable and robust with respect to the
variability of shape and distribution of filaments. In the cases of highly
defined filament structures, the presented method is able to bridge the gaps
among the detected fragments, thus improving their shape reconstruction. From a
preliminary "a posteriori" analysis of derived filament physical parameters,
the method appears potentially able to add a sufficient contribution to
complete and refine the filament reconstruction.
