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This study consolidates Multi-Level Starlet Segmentation (MLSS) andMulti-Level Starlet Optimal Segmentation (MLSOS), techniques forphotomicrograph segmentation that use starlet wavelet detail levels to separateareas of interest in an input image. Several segmentation levels can beobtained using Multi-Level Starlet Segmentation; after that, Matthewscorrelation coefficient (MCC) is used to choose an optimal segmentation level,giving rise to Multi-Level Starlet Optimal Segmentation. In this paper, MLSOSis employed to estimate the concentration of gold nanoparticles with diameteraround 47 nm, reducted on natural rubber membranes. These samples were used onthe construction of SERS/SERRS substrates and in the study of natural rubbermembranes with incorporated gold nanoparticles influence on Leishmaniabraziliensis physiology. Precision, recall and accuracy are used to evaluatethe segmentation performance, and MLSOS presents accuracy greater than 88% forthis application.

Estimating the concentration of gold nanoparticles incorporated on Natural Rubber membranes using Multi-Level Starlet Optimal Segmentation