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

Zafari, Sahar; Eerola, Tuomas; Ferreira, Paulo; Kälviäinen, Heikki; Bovik, Alan (2019-08-22)

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zafari_et_al_automated_segmentation_post_print.pdf (8.699Mb)
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Post-print / Final draft

Zafari, Sahar
Eerola, Tuomas
Ferreira, Paulo
Kälviäinen, Heikki
Bovik, Alan
22.08.2019

Lecture Notes in Computer Science

11678

113-125

Springer, Cham

School of Engineering Science

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© 2019 Springer Nature Switzerland AG.
https://doi.org/10.1007/978-3-030-29888-3_10
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2019102534808

Tiivistelmä

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-of-the-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.

Lähdeviite

Zafari, S., Eerola, T., Ferreira, P., Kälviäinen, H., Bovik, A., Automated Segmentation of Nanoparticles in BF TEM Images by U-Net Binarization and Branch and Bound, Computer Analysis of Images and Patterns, Springer Lecture Notes in Computer Science, LNCS Vol. 11678, pp. 113-125, 2019, Proceedings of the 18th International Conference on Computer Analysis of Images and Patterns (CAIP 2019), Salerno, Italy, 2019. DOI: https://doi.org/10.1007/978-3-030-29888-3_10

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