Morphological segmentation of FIB‐SEM data of highly porous media

Abstract

SummaryNanoporous materials play an important role in modern batteries as well as fuel cells. The materials microstructure needs to be analyzed as it determines the electrochemical properties. However, the microstructure is too fine to be resolved by microcomputed tomography. The method of choice to analyze the microstructure is focused ion beam nanotomography (FIB-SEM). However, the reconstruction of the porous 3D microstructure from FIB-SEM image data in general has been an unsolved problem so far. In this paper, we present a new method using morphological operations. First, features are extracted from the data. Subsequently, these features are combined to an initial segmentation, that is then refined by a constrained watershed transformation. We evaluate our method with synthetic data, generated by a simulation of the FIB-SEM imaging process. We compare the ground truth in the simulated data to the segmentation result. The new method is found to produce a much smaller error than existing techniques.

Nano-Tomography of Porous Geological Materials Using Focused Ion Beam-Scanning Electron Microscopy

“…Currently there is no general method for segmenting pores with background features, a feature common to geological materials, and thus the user must validate the results by comparing the final estimates of porosity with that expected from different section images. Segmentation methods are being adapted to combat these issues, for example the morphological segmentation method and automatic phase segmentation method presented in [45][46][47][48]89]. 3D information on pore interconnectivities has provided modellers with first-hand information about complex materials that allows them to simulate transport properties [43,74,76,90,91], thereby gaining further knowledge of how the pore shape and interactions influence fluid flow, particularly permeability and fluid velocity.…”

“…The most challenging aspect of using FIB-SEM for such 3D tomography is the treatment of the data, i.e., slice image stack, to produce representative reconstructions. This is a particularly important challenge for porous materials, where the porosity and size distribution of the pores are unknown prior to analysis, which makes the accuracy of the segmentation procedure of the internal microstructures difficult to evaluate [45][46][47][48][49]. Until now, there has still been no benchmark of the precision of the segmentation procedure, which is influenced by artefacts introduced by negative interactions between the beams and the material into the slice images during 3D acquisition.…”

Prediction of Effective Properties of Porous Carbon Electrodes from a Parametric 3D Random Morphological Model

“…To this end, two nanoporous carbon-based materials, used in electrodes of EDLCs, are investigated. The two samples are imaged with FIB-SEM and the images are segmented using a new segmentation algorithm using mathematical morphology as in (Prill et al, 2013). Using the segmented microstructures, a stochastic model is defined for a two-phase heterogeneous material.…”

“…Other methods include the ones shown in (Salzer et al, 2012), such as threshold backpropagation or valley detection. In this study we use the method presented in (Prill et al, 2013), based on mathematical morphology. Since it has been shown, that even accurately segmented FIB-SEM data can lead to false transport properties, we combine the automatic segmentation with stochastic modeling, as in (Hutzenlaub et al, 2013).…”

“…After imaging and cropping, the images stacks were segmented using the morphological algorithm presented in (Prill et al, 2013), which uses the shading effect present in the SEM images, as the result of the large depth of field of the instrument. The segmentation parameters were chosen manually to optimize the visual impression of the segmentation.…”