Automatic characterization of nanofiber assemblies by image texture analysis

This paper presents a machine vision system for the automatic characterization of the quality properties of nanofiber assemblies using image texture analysis techniques. The objective is to use a digital image of a nanofiber membrane in order to estimate the pore size distribution (PSD), fiber diameter distribution (FDD) and permeability of the assembly. The underlying idea is that, after textural features have been extracted from the image in the form of statistical descriptors, these descriptors can be related to the membrane properties using multivariate regression techniques. Two alternative feature extraction techniques are considered, a statistical-based approach (namely, gray-level co-occurrence matrix, GLCM) and a transform-based approach (based on the use of wavelet transforms). r nanofiber membranes fabricated by electrospinning are used as test beds of the proposed system. The estimation results are very satisfactory for all properties with any of the two feature extraction techniques, with the wavelet transform-based approach slightly outperforming the GLCM one. Whereas for the estimation of the PSD and FDD a scanning electron microscope image of the product needs to be available, it is shown that as far as permeability is concerned an image at a much lower magnification scale (i.e. an optical microscope one) is sufficient to provide an accurate property estimation. Based on these results, the proposed system represents a very promising step toward the complete automation of quality assessment procedures in nanomaterials processing.