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A novel interpolation-based model for the computation of the Gray Level Co-occurrence Matrix (GLCM) is presented. The model enables GLCM computation for any real-valued angles and offsets, as opposed to the traditional, lattice-based model. A texture directionality estimation algorithm is defined using the GLCM-derived correlation feature. The robustness of the algorithm with respect to image blur and additive Gaussian noise is evaluated. It is concluded that directionality estimation is robust to image blur and low noise levels. For high noise levels, the mean error increases but remains bounded. The performance of the directionality estimation algorithm is illustrated on fluorescence microscopy images of fibroblast cells. The algorithm was implemented in C++ and the source code is available in an openly accessible repository.

Modern imaging technologies enable the study of microstructural features, which require capturing the finest details in high-resolution gigapixel images. Nevertheless, the resolution disparity between gigapixel images and megapixel displays presents a challenge to effective visual analysis because subtle texture differences are hardly perceivable at coarser resolutions. In this paper, we present a hierarchical segmentation technique based on the joint distribution of intensity and noise-resistant local binary patterns to differentiate subtle microstructural textures across various scales. The coarse-to-fine segmentation procedure subdivides each parent segment into texturally-distinct child segments at progressively higher resolutions. The hierarchical structure of segments allows creating intermediate segmentation results interactively. Based on the intermediate results, we highlight regions with texture differences using distinct colors, which provide salient visual hints to users despite the current viewing resolution. Our new technique has been validated on large microscopy images and shows promising results.

There is currently no method for assessing the nature of the cell niche provided by 3D biomaterial scaffolds. Analyzing human bone marrow stromal cell (hBMSC) 3D cell shape in response to different biomaterial scaffolds allowed the 3D cell niche promoted by biomaterial scaffolds to be evaluated. Primary hBMSCs (p5) were seeded (5,000 cells/cm2) in 10 different biomaterial scaffolds and cultured for 24 h. Samples were fixed and stained for actin and nucleus, imaged with confocal microscopy to obtain a 3D volume (z-stack), and 3D cell shape was analyzed with computational approaches. Over 100 cells were imaged per scaffold group (10 scaffold groups, ~1250 cells total), resulting in the largest known 3D stem cell dataset (~135,000 files, ~135 GB) and enabling a high degree of statistical rigor. The images were segmented using an automated algorithm and a final dataset of 969 well-segmented cells were analyzed with 79 shape metrics, which enabled 3D cellular morphotyping of scaffold niches. The variety of scaffolds studied promoted different cell morphologies during culture and there were significant differences in shape metrics, particularly for cell depth, surface area, and volume. This study demonstrated a quantitative approach to analyze 3D cell shape and morphotype and is the largest known study analyzing 3D cell shape in response to a variety of biomaterial scaffolds. The dataset is publically accessible with an online 3D viewer. These results could inform the selection of prospective scaffolds for applications based on 3D cell shape in the tissue of interest.

Benzofulvenes-based monomers have been synthesized for the first time and have been polymerized by means of electrochemical methods onto different electrodic materials. Morphological investigation has been performed by using SEM; electrical and optical properties have been studied by means of both cyclic voltammetry, optical absorption and spectroelectrochemical techniques. Finally, solid state electrochromic devices have been fabricated. They have displayed high optical contrast.