Open AccessSegmentation of 3D FIB-SEM data with pore-back effect
Author(s) -
Iryna Reimers,
Ilia V. Safonov,
Ivan Yakimchuk
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1368/3/032015
Subject(s) - segmentation , metric (unit) , artificial intelligence , ground truth , focused ion beam , sample (material) , computer science , pixel , computer vision , image segmentation , pattern recognition (psychology) , materials science , engineering , physics , ion , operations management , quantum mechanics , thermodynamics
Digital rock physics is used for the investigation of oil and gas reservoirs. It involves various mathematical simulations on a digital representation of a rock sample, which is usually obtained with imaging techniques. Focused ion beam scanning electron microscopy (FIB-SEM) tomography provides high-resolution images of sequential layers of a sample, and segmentation of these images is a key stage in the construction of 3D digital rock. Conventional segmentation methods are not applicable for FIB-SEM images due to specific artifacts such as the pore-back effect. We propose a new segmentation algorithm that relies on the marker-controlled watershed, variance filter and morphological operations. The results are validated with the use of manually labelled ground truth data. Furthermore, we develop a new metric for evaluation of segmentation quality. This metric is based on analysis of segmented regions and, in the case of porous media, provides more reliable evaluation than pixel-wise measures.