Electrospun nanofiber scaffolds are important in biomedicine, especially skin tissue engineering, in which scaffold porosity and fiber orientation significantly affect cell penetration, nutrition diffusion, and biomechanical integration. Despite progress on image-based, non-destructive methods for SEM analysis, integrating quantitative metrics from multiple morphological descriptors into a unified and automated workflow remains challenging. In this paper, we present a software tool developed using MATLAB App Designer, which facilitates SEM image analysis for non-destructive characterization of electrospun nanofiber scaffolds. This application provides capability to determine porosity, pore size, fiber diameter and fractal dimension as well as estimation of BET surface area and Barrett-Joyner-Halenda (BJH) volume based on geometric models. Eight independently electrospun nanofiber scaffolds, fabricated at identical electrospinning parameters, were analyzed using a combination of automated thresholding, morphological processing, and skeletonization. For each sample, five SEM images were analyzed (total = 40 images). Estimated BET and BJH data were extrapolated from image-based parameters, considering cylindrical-like scaffold geometry to assess internal consistency. High correlations were observed between porosity and surface area, and pore size with BJH volume highlight that the model is appropriate for relative scaffold screening. Statistical comparison detected various methods (Otsu and morphological thresholding) were significantly different from each other (p R values were observed, they reflect internal coherence, not external validation. The tool offers a replicable platform for early-stage scaffold assessment in tissue engineering and nanomaterial research.
Kareem et al. (Tue,) studied this question.