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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