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INTRODUCTION: Procedures for measuring and counting tracks are time-consuming and involve practical problems. The precision of automatic counting methods is not satisfactory yet; the major challenges are distinguishing tracks and material defects, identifying small tracks and defects of similar size, and detecting overlapping tracks. MATERIALS AND METHODS: Here, we address the overlapping tracks issue using the algorithm Watershed Using Successive Erosions as Markers (WUSEM), which combines the watershed transform, morphological erosions and labeling to separate regions in photomicrographs. We tested this method in two data sets of diallyl phthalate (DAP) photomicrographs and compared the results when counting manually and using the classic watershed and H-watershed transforms. RESULTS: The mean automatic/manual efficiency counting ratio when using WUSEM in the test data sets is 0.97 ± 0.11. CONCLUSION: WUSEM shows reliable results when used in photomicrographs presenting almost isotropic objects. Also, diameter and eccentricity criteria may be used to increase the reliability of this method.
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Image segmentation, the process of separating the elements within a picture, is frequently used for obtaining information from photomicrographs. Segmentation methods should be used with reservations, since incorrect results can mislead when interpreting regions of interest (ROI). This decreases the success rate of extra procedures. Multi-Level Starlet Segmentation (MLSS) and Multi-Level Starlet Optimal Segmentation (MLSOS) were developed to be an alternative for general segmentation tools. These methods gave rise to Jansen-MIDAS, an open-source software. A scientist can use it to obtain several segmentations of hers/his photomicrographs. It is a reliable alternative to process different types of photomicrographs: previous versions of Jansen-MIDAS were used to segment ROI in photomicrographs of two different materials, with an accuracy superior to 89%.
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Electronic microscopy has been used for morphology evaluation of different materials structures. However, microscopy results may be affected by several factors. Image processing methods can be used to correct and improve the quality of these results. In this article, we propose an algorithm based on starlets to perform the segmentation of scanning electron microscopy images. An application is presented in order to locate gold nanoparticles in natural rubber membranes. In this application, our method showed accuracy greater than 85% for all test images. Results given by this method will be used in future studies, to computationally estimate the density distribution of gold nanoparticles in natural rubber samples and to predict reduction kinetics of gold nanoparticles at different time periods.