Correction of Scanning Electron Microscope Imaging Artifacts in a Novel Digital Image Correlation Framework

Maraghechi, Siavash; Hoefnagels, Jpm Johan; Peerlings, R.H.J.; Rokoš, Ondřej; Geers, M.G.D.

doi:10.1007/s11340-018-00469-w

Cited by 35 publications

(36 citation statements)

References 39 publications

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“…a distortion that shows an uneven distribution of the pixel size 5,6 . After alignment of the dataset by SIFT, these deformations are especially prominent in the upper and lower parts of the stack, while with TM, the deformations result in a poor alignment quality further away from the template structure, hence at the bottom of the image (Fig.…”

mentioning

confidence: 99%

AMST: Alignment to Median Smoothed Template for Focused Ion Beam Scanning Electron Microscopy Image Stacks

Hennies

Lleti

Schieber

et al. 2020

Sci Rep

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Alignment of stacks of serial images generated by focused ion Beam Scanning electron Microscopy (FIB-SEM) is generally performed using translations only, either through slice-by-slice alignments with SIFT or alignment by template matching. However, limitations of these methods are twofold: the introduction of a bias along the dataset in the z-direction which seriously alters the morphology of observed organelles and a missing compensation for pixel size variations inherent to the image acquisition itself. These pixel size variations result in local misalignments and jumps of a few nanometers in the image data that can compromise downstream image analysis. We introduce a novel approach which enables affine transformations to overcome local misalignments while avoiding the danger of introducing a scaling, rotation or shearing trend along the dataset. Our method first computes a template dataset with an alignment method restricted to translations only. This pre-aligned dataset is then smoothed selectively along the z-axis with a median filter, creating a template to which the raw data is aligned using affine transformations. Our method was applied to FIB-SEM datasets and showed clear improvement of the alignment along the z-axis resulting in a significantly more accurate automatic boundary segmentation using a convolutional neural network. In recent years, the development of new electron microscopy technologies allowed the automated serial imaging of entire biological specimens, from cells to model organisms. Amongst these techniques, Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) has emerged as a preferred technology for gathering serial images at isotropic resolution. After volumetric acquisition, an important step for proper visualization and accurate morphometric analysis is the alignment of the image stack along the z-axis. However, due to the size and complexity of the data, alignments using simple translations are most commonly used 1,2 instead of adapting the transformations to the specific type of data. Consequently, the most common algorithms used to find correlation between adjacent slices are alignment by SIFT 3 and alignment using a template structure matched by cross correlation, also known as template matching (TM). In the specific case of data acquired using the Atlas 5 software 4 , TM is efficiently performed on markings created at the surface of the sample (Fig. 1a). These markings are at a constant position with respect to the flat sample surface. In case of SIFT when only global translations are applied, each slice is aligned to the previous, preserving local morphological properties (a few slices) along the z-axis, while disturbing the global shape of objects across long distances. As a consequence, straight objects, such as the sample surface plane, can become crooked in a non-predictable manner (Fig. 2a). Additionally, for FIB-SEM data, slices can project distorted images when different parts of an imaged cross-section are exposed to different rates of radiation. This effect typically...

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mentioning

confidence: 99%

AMST: Alignment to Median Smoothed Template for Focused Ion Beam Scanning Electron Microscopy Image Stacks

Hennies

Lleti

Schieber

et al. 2020

Sci Rep

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“…By comparing adjacent pairs of images, the drift can be measured using matching techniques. Concretely, template matching [201], feature matching [46], phase correlation [202] can be employed in this process. To model the drift-time relationship, the drift velocity at pixel positions in the FOV is commonly fitted with the B-spline function in time.…”

Section: Error Source Analysismentioning

confidence: 99%

A Review of Computer Microvision-Based Precision Motion Measurement: Principles, Characteristics, and Applications

Sheng

Pang

et al. 2021

IEEE Trans. Instrum. Meas.

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Micro/nano engineering is an emerging field which enables engineering and scientific discoveries in the microworld. As an effective and powerful tool for automation and manipulation at small scales, precision motion measurement by computer micro-vision is now broadly accepted and widely used in micro/nano engineering. Unlike other measurement methods, the vision-based techniques can intuitively visualize the measuring process with high interactivity, expansibility, and flexibility. This review paper aims to comprehensively present a survey of micro-vision-based motion measurement from the collective experience. Working principles of micro-vision systems are firstly introduced and described, where the hardware configuration, model calibration, and motion measurement algorithms are systematically summarized. The characteristics and performances of different micro-vision-based methods are then analyzed and discussed in terms of measurement resolution, range, degree of freedom, efficiency, and error sources. Recent advances of applications empowered by the developed computer micro-visionbased methods are also presented. The review can be helpful to researchers who engage in the development of micro-vision-based techniques, as well as provide the recent state and tendency for the research community of vision-based measurement, manipulation, and automation at micro/nano scales.

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“…Images are typically affected by optical distortions of the objective lens. These spatial distortions are measured using the method proposed by Maraghechi et al (2018Maraghechi et al ( , 2019. The measured distortion field is used to correct the images taken during the in-situ tests, which is necessary because the magnitude of the distortions is roughly 10% of the actual displacements in the metamaterial specimens.…”

Section: Full-field Displacement Measurementmentioning

confidence: 99%

Experimental full-field analysis of size effects in miniaturized cellular elastomeric metamaterials

et al. 2020

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Cellular elastomeric metamaterials are interesting for various applications, e.g. soft robotics, as they may exhibit multiple microstructural pattern transformations, each with its characteristic mechanical behavior. Numerical literature studies revealed that pattern formation is restricted in (thick) boundary layers causing significant mechanical size effects. This paper aims to experimentally validate these findings on miniaturized specimens, relevant for real applications, and to investigate the effect of increased geometrical and material imperfections resulting from specimen miniaturization. To this end, miniaturized cellular metamaterial specimens are manufactured with different scale ratios, subjected to in-situ micro-compression tests combined with digital image correlation yielding full-field kinematics, and compared to complementary numerical simulations. The specimens global behavior agrees well with the numerical predictions, in terms of pre-buckling stiffness, buckling strain and post-buckling stress. Their local behavior, i.e. pattern transformation and boundary layer formation, is also consistent between experiments and simulations. Comparison of these results with idealized numerical studies from literature reveals the influence of the boundary conditions in real cellular metamaterial applications, e.g. lateral confinement, on the mechanical response in terms of size effects and boundary layer formation.

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Correction of Scanning Electron Microscope Imaging Artifacts in a Novel Digital Image Correlation Framework

Cited by 35 publications

References 39 publications

AMST: Alignment to Median Smoothed Template for Focused Ion Beam Scanning Electron Microscopy Image Stacks

AMST: Alignment to Median Smoothed Template for Focused Ion Beam Scanning Electron Microscopy Image Stacks

A Review of Computer Microvision-Based Precision Motion Measurement: Principles, Characteristics, and Applications

Experimental full-field analysis of size effects in miniaturized cellular elastomeric metamaterials

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