An Evaluation Method for Reconstructed Images in Electrical Tomography

Wan, Chao; Qian, Xiangchen; Yan, Yong; Dong, Feng; Wang, Huaxiang

doi:10.1109/imtc.2008.4547125

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…We found that commonly used evaluation methods such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) [16] did not correspond to the visual quality of the final images in a reliable way. Therefore, we additionally used the Complex Wavelet Structural Similarity (CW-SSIM) [14] that is insensitive to geometric distortions that do not influence structure, which is an important property for the application in this study.…”

Section: Evaluation Methodsmentioning

confidence: 96%

How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?

Trampert

Bourghorbel²,

Potocek³

et al. 2018

Ultramicroscopy

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In scanning electron microscopy, the achievable image quality is often limited by a maximum feasible acquisition time per dataset. Particularly with regard to three-dimensional or large field-of-view imaging, a compromise must be found between a high amount of shot noise, which leads to a low signal-to-noise ratio, and excessive acquisition times. Assuming a fixed acquisition time per frame, we compared three different strategies for algorithm-assisted image acquisition in scanning electron microscopy. We evaluated (1) raster scanning with a reduced dwell time per pixel followed by a state-of-the-art Denoising algorithm, (2) raster scanning with a decreased resolution in conjunction with a state-of-the-art Super Resolution algorithm, and (3) a sparse scanning approach where a fixed percentage of pixels is visited by the beam in combination with state-of-the-art inpainting algorithms. Additionally, we considered increased beam currents for each of the strategies. The experiments showed that sparse scanning using an appropriate reconstruction technique was superior to the other strategies.

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Section: Evaluation Methodsmentioning

confidence: 96%

How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?

Trampert

Bourghorbel²,

Potocek³

et al. 2018

Ultramicroscopy

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show abstract

“…The f-FCM algorithm is used to cluster all pixels in any ET image that is reconstructed by the three most used imaging algorithms: LBP, Tikhonov and Landweber algorithms respectively, available in our group in Tianjin Key Laboratory of Process Measurement and Control, Tianjin University 2,12 . For any image, each dominant individual color corresponds to a determined grey degree.…”

Section: Evaluation Of Et Imaging Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Electrical Tomography (ET) has gained considerable interest in both industrial measurement and biomedical engineering 2,3 . Although great progress has been made in improving ET technique in the past two decades, the quality of ET images such as resolution is less satisfactory, mainly due to the known fact that the image reconstruction is an inverse problem with illconditioned property 2,3,4 . Though much work has been done by researchers improve ET image reconstruction, the state-of-art imaging resolution assessment is still limited to single-parameter evaluation of binary images.…”

Section: Introductionmentioning

confidence: 99%

“…The applicability to color image from ET is not explored. Thirdly, though the methods consider multi-parameters, the parameters studied are relatively simple, such as images error resolution, duty ratio 2,9 . Lastly, some methods only take a known mesh instead of the whole image to identify the "optimal" parameter settings during the reconstruction and evaluation 7,9 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fused Multi-Characteristic Validity Index: An Application to Reconstructed Image Evaluation in Electrical Tomography

Yue¹,

Wu²,

Zhao³

et al. 2011

IJCIS

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The quality of reconstructed images is an important and direct criterion to quantitatively evaluate the effectiveness of reconstruction algorithms in electrical tomography (ET). Unfortunately, there lacks of effective and efficient approach to assessing the quality of ET images in literature and practices. Realizing the gap, we recently develop a novel index termed fused Multiple Characteristic Indices (fMCI) 1 aiming to measure the quality of clustering various data sets including imaging data (e.g., ET). In this paper, we propose a method based on fMCI to quantitatively evaluate the quality of reconstructed images. The method first applies the fast fuzzy c-means algorithm to cluster pixels in the reconstruct image. The fMCI is then applied to evaluate the clustering results and image quality. Simulated data derived from finite element method is used to demonstrate that the proposed method is capable to evaluate the quality of the reconstructed images and the results are consistent with visual perception. In addition, a number of experiments are conducted to demonstrate the applicability and effectiveness of the fMCI based method for image quality assessment.

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Exemplar-based inpainting as a solution to the missing wedge problem in electron tomography

Trampert

Chen

et al. 2018

Ultramicroscopy

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An Evaluation Method for Reconstructed Images in Electrical Tomography

Cited by 8 publications

References 14 publications

How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?

How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?

Fused Multi-Characteristic Validity Index: An Application to Reconstructed Image Evaluation in Electrical Tomography

Exemplar-based inpainting as a solution to the missing wedge problem in electron tomography

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