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research-article

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
aryeh@cc.huji.ac.il

Itzhak Halevy

Department of Physics IAEC-NRCN Beer-Sheva 84190 Israel
halevy.itzhak.dr@gmail.com

Naida Dzigal

SGAS Laboratories IAEA-Seibersdorf, A-1400 Vienna Austria
naida.dzigal89@gmail.com

Ernesto Chinea-Cano

SGAS Laboratories IAEA-Seibersdorf, A-1400 Vienna Austria
chineaernesto@gmail.com

Uri Admon

Department of Materials IAEC-NRCN Beer-Sheva 84190 Israel
uadmon@gmail.com

1Corresponding author.

ASME doi:10.1115/1.4036434 History: Received September 28, 2016; Revised March 23, 2017

Abstract

Detection of microscopic fission track (FT) star-shaped clusters, developed in SSNTD by etching, created by fission fragments emitted from fissile particles irradiated by neutrons, is a key technique in nuclear forensics and safeguards investigation. It involves scanning and imaging a large area, typically 1-2 sq.cm, 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 artefacts present in the image. This task, if done manually, is time consuming, operator dependent, and prone to human errors. To solve the problem, an automated workflow have been developed for (a) scanning large area detectors, in order to acquire large images with adequate high resolution, and (b) an image processing scheme, implemented in ImageJ, to automatically detect the FT clusters. The scheme combines intensity-based segmentation approaches, a morphological algorithm capable of detecting and counting endpoints in putative FT clusters and thus enables rejection of non-FT artefacts. In this paper, the methodology is described and first very promising results shown.

Copyright (c) 2017 by ASME
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