  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.
