ALGORITHMS OF GENERATION AND SEM-IMAGES PROCESSING FOR PROPERTIES IDENTIFICATION OF BIOINORGANIC MATRICES AND METHODS OF THEIR VERIFICATION
Abstract
Scanning electron microscopy (SEM) is one of the most common methods for analyzing the characteristics of materials obtained through chemical synthesis. The use of this method makes it possible to obtain images with high resolution and magnification. The article examines algorithms for image analysis of materials with specific properties, such as porosity – bioneorganic matrices. Scaffolds are a broad class of materials with a wide range of applications, including agriculture, medicine, catalysis, and many others. One of the important applications of such structures is tissue engineering, where such frameworks are necessary to ensure the regenerative processes of body tissues. And for each organism matrices must be personalized, which requires a laborious process of selecting the characteristics of the framework applicable in a particular case. This task is currently partially solved by the application of artificial intelligence technologies to improve accuracy or support decision making during matrix fabrication or analysis. However, some of the work in this process is still manual and represents a labor-intensive chore for the technician. In particular, the process of analyzing SEM images and characterizing the resulting material still involves many time-consuming steps using various tools. At the same time, such characteristics as porosity, tortuosity, and diffusivity are very important factors for an expert in the process of making a decision on the applicability of the fabricated bioinorganic matrix in each specific case. Accordingly, the purpose of this research is to develop a set of algorithms for processing SEM-images. Also based on the set goal within the framework of the research we can distinguish a number of issues: development of algorithms for detection of objects in the image, development of a neural network model for refining the detection results, implementation of algorithms for calculating the characteristics of porous material, as well as design and execution of a number of verification tests to confirm the quality of the performed calculations. As a result of our research, we drew some conclusions. In particular, we found that an approach using synthetic data generation significantly speeds up and simplifies the learning process of neural networks, as well as improves the quality of output models. We also found that the algorithms we developed can fully automate the analysis of SEM images with porous structures, and their quality was confirmed through a number of verification tests. These algorithms can be applied to other similar problems related to image analysis and identification of features and characteristics.
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