  This study consolidates Multi-Level Starlet Segmentation (MLSS) and
Multi-Level Starlet Optimal Segmentation (MLSOS), techniques for
photomicrograph segmentation that use starlet wavelet detail levels to separate
areas of interest in an input image. Several segmentation levels can be
obtained using Multi-Level Starlet Segmentation; after that, Matthews
correlation coefficient (MCC) is used to choose an optimal segmentation level,
giving rise to Multi-Level Starlet Optimal Segmentation. In this paper, MLSOS
is employed to estimate the concentration of gold nanoparticles with diameter
around 47 nm, reducted on natural rubber membranes. These samples were used on
the construction of SERS/SERRS substrates and in the study of natural rubber
membranes with incorporated gold nanoparticles influence on Leishmania
braziliensis physiology. Precision, recall and accuracy are used to evaluate
the segmentation performance, and MLSOS presents accuracy greater than 88% for
this application.
