An automatic method for segmentation of fission tracks in epidote crystal photomicrographs

TL;DR

This paper introduces an automatic segmentation method using starlet wavelets for identifying fission tracks in mineral photomicrographs, significantly improving speed and accuracy over manual methods.

Contribution

It presents a novel automated technique based on starlet wavelets for segmenting fission tracks, with validated high accuracy on epidote crystal images.

Findings

Achieved over 89% accuracy in fission track segmentation.

Automated method reduces observer bias and speeds up analysis.

Available algorithms facilitate easy application by users.

Abstract

Manual identification of fission tracks has practical problems, such as variation due to observer-observation efficiency. An automatic processing method that could identify fission tracks in a photomicrograph could solve this problem and improve the speed of track counting. However, separation of non-trivial images is one of the most difficult tasks in image processing. Several commercial and free softwares are available, but these softwares are meant to be used in specific images. In this paper, an automatic method based on starlet wavelets is presented in order to separate fission tracks in mineral photomicrographs. Automatization is obtained by Matthews correlation coefficient, and results are evaluated by precision, recall and accuracy. This technique is an improvement of a method aimed at segmentation of scanning electron microscopy images. This method is applied in photomicrographs of epidote phenocrystals, in which accuracy higher than 89% was obtained in fission track segmentation, even for difficult images. Algorithms corresponding to the proposed method are available for download. Using the method presented here, an user could easily determine fission tracks in photomicrographs of mineral samples.