Special Section Papers

Fission Track Detection Using Automated Microscopy

[+] Author and Article Information
Aryeh M. Weiss

Faculty of Engineering and
Nanotechnology Center,
Bar Ilan University,
Ramat Gan 52900, Israel
e-mail: aryeh@cc.huji.ac.il

Itzhak Halevy

Department of Physics,
Beer-Sheva 84190, Israel

Naida Dziga, Ernesto Chinea-Cano

SGAS Laboratories,
Vienna A-1400l, Austria

Uri Admon

Department of Materials,
Beer-Sheva 84190, Israel

1Corresponding author.

Manuscript received September 28, 2016; final manuscript received March 23, 2017; published online May 25, 2017. Assoc. Editor: Jean Koch.

ASME J of Nuclear Rad Sci 3(3), 030910 (May 25, 2017) (7 pages) Paper No: NERS-16-1116; doi: 10.1115/1.4036434 History: Received September 28, 2016; Revised March 23, 2017

Detection of microscopic fission track (FT) star-shaped clusters, developed in a solid state nuclear track detector (SSNTD) by etching, created by fission fragments emitted from particles of fissile materials irradiated by neutrons, is a key technique in nuclear forensics and safeguards investigation. It involves scanning and imaging of a large area, typically 100–400 mm2, of a translucent SSNTD (e.g., polycarbonate sheet, mica, etc.) to identify the FT clusters, sparse as they may be, that must be distinguished from dirt and other artifacts present in the image. This task, if done manually, is time consuming, operator dependent, and prone to human errors. To solve this problem, an automated workflow has been developed for (a) scanning large area detectors, in order to acquire large images with adequate high resolution, and (b) processing the images with a scheme, implemented in ImageJ, to automatically detect the FT clusters. The scheme combines intensity-based segmentation approaches with a morphological algorithm capable of detecting and counting endpoints in putative FT clusters in order to reject non-FT artifacts. In this paper, the workflow is described, and very promising preliminary results are shown.

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Fig. 2

The software must distinguish between (a) a valid FT and (b) a typical artifact

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Fig. 1

Three types of FT clusters developed in lexan (polycarbonate SSNTD), by etching with NaOH

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Fig. 3

(a) Image of a detector showing the line (drawn black) which was used for the line profiles in (b) and (c), (b) line profile before application of 3 × 3 median filter, and (c) line profile after application of 3 × 3 median filter

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Fig. 4

(a) Original image, (b) same image after application of Gaussian blur (σ = 100 pixels), and (c) result of (b) and (a)

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Fig. 5

(a) Binary image obtained by hysteresis thresholding and (b) image in (a) filtered on area, removing all objects containing less than 500 pixels

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Fig. 6

(a) Original brightfield image of a high-intensity FT cluster; (b) segmented FT cluster: small artifacts and objects which do not meet the criteria for being a valid FT cluster are excluded; (c) image A with endpoints marked with white spots, after skeleton analysis; and (d) Skeletonized FT cluster

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Fig. 7

(a) Image of detector that consists of 130 tiled fields and (b) same image, with field boundaries marked in white

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Fig. 8

(a) Image acquired with Nikon DS-Qi2 16 MP imager, 72 fields, about 27 mm × 20 mm. All three fiducial markers are included in this image, (b) zoom-in to the area marked in (a), about 6 mm × 5 mm, and (c) zoom-in to the area marked in (b), about 0.95 mm × 0.92 mm.

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Fig. 9

Typical input image with many artifacts, and a few FT, which were identified by the software and marked with black squares. The width of this 10 × 10 mosaic image is about 12 mm.

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Fig. 10

(a)–(d) The four FT clusters that were found in Fig. 9, and (e) a very weak cluster that was not identified by the program



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