Automatic segmentation of inorganic nanoparticles in BF TEM micrographs

Groom, Daniel J.; Yu, Kun; Rasouli, Somaye; Polarinakis, J.; Bovik, Alan C.; Ferreira, Paulo J.

doi:10.1016/j.ultramic.2018.06.002

Cited by 23 publications

(27 citation statements)

References 10 publications

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“…This makes it possible to detect the concave points robustly and further allows accurate segmentation of individual NPs. We show that the proposed method outperforms five existing methods [6,20,22,26,14] with higher NP detection rate and segmentation accuracy.…”

Section: Introductionmentioning

confidence: 76%

“…The experiments were carried out using a dataset from an interesting real-world application [6]. The dataset consists of BF TEM images of nanocatalyst NPs, present in cathode of a proton exchange fuel cell captured in a JEOL 2010F operated 200kV.…”

Section: Datamentioning

confidence: 99%

“…It should be noted that, in the original publication [6], the total number of NPs was reported as 2000. This is due to the fact that the paper considered only non-overlapping NPs.…”

Section: Datamentioning

confidence: 99%

“…However, studies of Inorganic NPs using TEM have suffered from poor statistical quality due to the difficulty of their segmentation in the image processing step. Although, the recent studies have made significant progress towards remedying the problem of inaccurate segmentation and the resultant poor statistical information [6,24,20], segmenting Inorganic NPs in TEM micrographs still rely on either global thresholding or local thresholding [5,2]. Global thresholding [13,7] has performed successfully in the past in some limited cases where the NPs are clearly distinguishable form background [20].…”

Section: Introductionmentioning

confidence: 99%

“…However, this binarization technique also reproducibly fails to distinguish between the background and the NPs. A recent study [6] has proposed the use of variance hybridized mean local thresholding as a binarization routine, and this method shows promising results. When compared to the baseline of global thresholding, this novel binarization routine effectively eliminates false negatives while greatly reducing false positives, usually providing excellent segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Automated Segmentation of Nanoparticles in BF TEM Images by U-Net Binarization and Branch and Bound

Zafari

Eerola

Ferreira

et al. 2019

Computer Analysis of Images and Patterns

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Transmission electron microscopy (TEM) provides information about Inorganic nanoparticles that no other method is able to deliver. Yet, a major task when studying Inorganic nanoparticles using TEM is the automated analysis of the images, i.e. segmentation of individual nanoparticles. The current state-ofthe-art methods generally rely on binarization routines that require parameterization, and on methods to segment the overlapping nanoparticles (NPs) using highly idealized nanoparticle shape models. It is unclear, however, that there is any way to determine the best set of parameters providing an optimal segmentation, given the great diversity of NPs characteristics, such as shape and size, that may be encountered. Towards remedying these barriers, this paper introduces a method for segmentation of NPs in Bright Field (BF) TEM images. The proposed method involves three main steps: binarization, contour evidence extraction, and contour estimation. For the binarization, a model based on the U-Net architecture is trained to convert an input image into its binarized version. The contour evidence extraction starts by recovering contour segments from a binarized image using concave contour points detection. The contour segments which belong to the same nanoparticle are grouped in the segment grouping step. The grouping is formulated as a combinatorial optimization problem and solved using the well-known branch and bound algorithm. Finally, the full contours of the NPs are estimated by an ellipse. The experiments on a real-world dataset consisting of 150 BF TEM images containing approximately 2,700 NPs show that the proposed method outperforms five current state-of-art approaches in the overlapping NPs segmentation.

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Section: Introductionmentioning

confidence: 76%

Section: Datamentioning

confidence: 99%

“…It should be noted that, in the original publication [6], the total number of NPs was reported as 2000. This is due to the fact that the paper considered only non-overlapping NPs.…”

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated Segmentation of Nanoparticles in BF TEM Images by U-Net Binarization and Branch and Bound

Zafari

Eerola

Ferreira

et al. 2019

Computer Analysis of Images and Patterns

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Unsupervised Learning for the Segmentation of Small Crystalline Particles at the Atomic Level

Bárcena-González

Hernandez-Robles

Mayoral

et al. 2023

Cryst. Res. Technol.

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Electron backscattering diffraction provides the analysis of crystalline phases at large scales (microns) while precession electron diffraction may be used to get 4D-STEM data to elucidate structure at nanometric resolution. Both are limited by the probe size and also exhibit some difficulties for the generation of large datasets, given the inherent complexity of image acquisition. The latter appoints the application of advanced machine learning techniques, such as deep learning adapted for several tasks, including pattern matching, image segmentation, etc. This research aims to show how Gabor filters provide an appropriate feature extraction technique for electron microscopy images that could prevent the need of large volumes of data to train deep learning models. The work presented herein combines an algorithm based on Gabor filters for feature extraction and an unsupervised learning method to perform particle segmentation of polyhedral metallic nanoparticles and crystal orientation mapping at atomic scale. Experimental results have shown that Gabor filters are convenient for electron microscopy images analysis, that even a nonsupervised learning algorithm can provide remarkable results in crystal segmentation of individual nanoparticles. This approach enables its application to dynamic analysis of particle transformation recorded with aberration-corrected microscopy, offering new possibilities of analysis at nanometric scale.

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The determination of optimum segmentation parameters using genetic algorithms: Application to different segmentation algorithms and transmission electron microscopy tomography reconstructed volumes

Martinez

Okariz

Iturrondobeitia

et al. 2023

Microscopy Res & Technique

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A method for optimizing an automatic selection of values for parameters that feed segmentation algorithms is proposed. Evolutionary optimization techniques in combination with a fitness function based on a mutual information parameter have been used to find the optimal parameter values of region growing, fuzzy c‐means and graph cut segmentation algorithms. To validate the method, the segmentation of two transmission electron microscopy tomography reconstructed volumes of a carbon black‐reinforced rubber and a polylactic acid and clay nanocomposite is carried out (i) using evolutionary optimization techniques and (ii) manually by experts. The results confirm that the use of evolutionary optimization techniques, such as genetic algorithms, reduces the computational operation cost needed for a total grid search of segmentation parameters, reducing the probability of reaching a false optimum, and improving the segmentation quality.Highlights A new approach to optimize 3D segmentation algorithms. Methodology to optimize segmentation parameters and improve segmentation quality. Improvement on the results when using region growing, fuzzy c‐means and graph cuts algorithms.

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Automatic segmentation of inorganic nanoparticles in BF TEM micrographs

Cited by 23 publications

References 10 publications

Automated Segmentation of Nanoparticles in BF TEM Images by U-Net Binarization and Branch and Bound

Automated Segmentation of Nanoparticles in BF TEM Images by U-Net Binarization and Branch and Bound

Unsupervised Learning for the Segmentation of Small Crystalline Particles at the Atomic Level

The determination of optimum segmentation parameters using genetic algorithms: Application to different segmentation algorithms and transmission electron microscopy tomography reconstructed volumes

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