  The Liquid Argon Time Projection Chamber (LAr-TPC) detectors provide
excellent imaging and particle identification ability for studying neutrinos.
An efficient and automatic reconstruction procedures are required to exploit
potential of this imaging technology. Herein, a novel method for segmentation
of images from LAr-TPC detectors is presented. The proposed approach computes a
feature descriptor for each pixel in the image, which characterizes amplitude
distribution in pixel and its neighbourhood. The supervised classifier is
employed to distinguish between pixels representing particle's track and noise.
The classifier is trained and evaluated on the hand-labeled dataset. The
proposed approach can be a preprocessing step for reconstructing algorithms
working directly on detector images.
