Image processing and fractal box counting: user-assisted method for multi-scale porous scaffold characterization.

Image analysis has gained new effort in the scientific community due to the chance of investigating morphological properties of three dimensional structures starting from their bi-dimensional gray-scale representation. Such ability makes it particularly interesting for tissue engineering (TE) purposes. Indeed, the capability of obtaining and interpreting images of tissue scaffolds, extracting morphological and structural information, is essential to the characterization and design of engineered porous systems. In this work, the traditional image analysis approach has been coupled with a probabilistic based percolation method to outline a general procedure for analysing tissue scaffold SEM micrographs. To this aim a case study constituted by PCL multi-scaled porous scaffolds was adopted. Moreover, the resulting data were compared with the outputs of conventionally used techniques, such as mercury intrusion porosimetry. Results indicate that image processing methods well fit the porosity features of PCL scaffolds, overcoming the limits of the more invasive porosimetry techniques. Also the cut off resolution of such IP methods was discussed. Moreover, the fractal dimension of percolating clusters, within the pore populations, was addressed as a good indication of the interconnection degree of PCL bi-modal scaffolds. Such findings represent (i) the bases for a novel approach complementary to the conventional experimental procedure used for the morphological analysis of TE scaffolds, in particular offering a valid method for the analysis of soft materials (i.e., gels); also (ii) providing a new perspective for further studies integrating to the structural and morphological data, fluid-dynamics and transport properties modelling.