Accurate segmentation of dense nanoparticles by partially discrete electron tomography

Roelandts, Tom; Batenburg, Kees Joost; Biermans, Ellen; Kübel, Christian; Bals, Sara; Sijbers, Jan

doi:10.1016/j.ultramic.2011.12.003

Cited by 42 publications

(34 citation statements)

References 26 publications

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“…SIRT was implemented as defined in [14], performing 300 iterations. (4) PDART [15] (Partially Discrete Algebraic Reconstruction Technique) is a partially discrete technique that assumes that only the densest material is homogeneous. The gray level of the densest material is exploited as prior knowledge.…”

Section: Experiments and Resultsmentioning

confidence: 99%

The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography

Roelandts

Batenburg

Dekker

et al. 2014

Computer Vision and Image Understanding

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a b s t r a c tIn this paper, we present the reconstructed residual error, which evaluates the quality of a given segmentation of a reconstructed image in tomography. This novel evaluation method, which is independent of the methods that were used to reconstruct and segment the image, is applicable to segmentations that are based on the density of the scanned object. It provides a spatial map of the errors in the segmented image, based on the projection data. The reconstructed residual error is a reconstruction of the difference between the recorded data and the forward projection of that segmented image. The properties and applications of the algorithm are verified experimentally through simulations and experimental micro-CT data. The experiments show that the reconstructed residual error is close to the true error, that it can improve gray level estimates, and that it can help discriminating between different segmentations.

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Section: Experiments and Resultsmentioning

confidence: 99%

The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography

Roelandts

Batenburg

Dekker

et al. 2014

Computer Vision and Image Understanding

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“…It has been shown already that by utilizing discrete tomography techniques, very accurate reconstructions can often be computed from only a few projection images [18]- [20]. In this paper, it will be demonstrated that discrete tomography can also be used for benefits in a different direction, namely to increase the resolution of the reconstructed images with the same (or only slightly less) number of projection angles.…”

Section: N X-ray Computed Tomography (Ct)mentioning

confidence: 93%

Super-Resolution for Computed Tomography Based on Discrete Tomography

Aarle

Batenburg

Gompel³

et al. 2014

IEEE Trans. on Image Process.

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Abstract-In computed tomography (CT), partial volume effects impede accurate segmentation of structures that are small with respect to the pixel size. In this paper, it is shown that for objects consisting of a small number of homogeneous materials, the reconstruction resolution can be substantially increased without altering the acquisition process. A super-resolution reconstruction approach is introduced that is based on discrete tomography, in which prior knowledge about the materials in the object is assumed. Discrete tomography has already been used to create reconstructions from a low number of projection angles, but in this paper, it is demonstrated that it can also be applied to increase the reconstruction resolution. Experiments on simulated and real μCT data of bone and foam structures show that the proposed method indeed leads to significantly improved structure segmentation and quantification compared with what can be achieved from conventional reconstructions.

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“…The next step is the reconstruction of the tomogram, typically using weighted back-projection (WP) or the simultaneous iterative reconstruction technique (SIRT) [3,24]. Nonetheless, much effort is being put into novel reconstruction algorithms which can notably improve the quality of tomograms, even with low sampling and limited angular range, using either the discretisation of intensities, the calculation of sinusoidal trajectories in sinograms, the incorporation of geometric prior knowledge, or compressive sensing [27][28][29][30][31]. Tomograms obtained after the reconstruction are 3D datasets containing a continuous range of intensity levels, and therefore in order to extract useful information, visualisation and segmentation are required in combination with the application of sophisticated algorithms, such as anisotropic non-linear diffusion, to reduce noise and enhance local structure without worsening the resolution or structural information, adaptive thresholding, or equalisation in real space [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Removing the effects of the “dark matter” in tomography

Gontard

2015

Ultramicroscopy

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a b s t r a c tElectron tomography (ET) using different imaging modes has been progressively consolidating its position as a key tool in materials science. The fidelity of a tomographic reconstruction, or tomogram, is affected by several experimental factors. Most often, an unrealistic cloud of intensity that does not correspond to a real material phase of the specimen ("dark matter") blurs the tomograms and enhances artefacts arising from the missing wedge (MW). Here we show that by simple preprocessing of the background level of any tomographic tilt series, it is possible to minimise the negative effects of that "dark matter". Iterative reconstruction algorithms converge better, leading to tomograms with fewer streaking artefacts from the MW, more contrast, and increased accuracy. The conclusions are valid irrespective of the imaging mode used, and the methodology improves the segmentation and visualisation of tomograms of both crystalline and amorphous materials. We show examples of HAADF STEM and BF TEM tomography.

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Accurate segmentation of dense nanoparticles by partially discrete electron tomography

Cited by 42 publications

References 26 publications

The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography

The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography

Super-Resolution for Computed Tomography Based on Discrete Tomography

Removing the effects of the “dark matter” in tomography

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