Doku Mühendisliğinde Malzemeye Ve Hücresel Özelliklere Ait İmgelerin Görüntü İşleme Teknikleri İle Değerlendirilmesi

Abstract

Image processing techniques are frequently used to obtain quantitative information from images of various imaging techniques. Image analysis in tissue engineering and cell biology is a time-consuming and specialized process. In addition, evaluation of the results may be subjective. Therefore, computer-based learning/vision-based applications have been developed rapidly in recent years. Instead of user-dependent software (ImageJ, DiameterJ etc.), semi-automatic or full-automatic software is preferred depending on these applications. Repeatable results are obtained with the preferred automated softwares to reduce the user effect, and the correct interpretation of the results is ensured. The use of automated software also prevents the researcher from misleading in future work. There are various studies in cell and tissue engineering using automated software to determine cell and material properties. In these studies, characteristics of cells such as cell type, cell viability, cell number and properties of fiber such as fiber diameter, fiber orientation, pore size were investigated. This study consists of two parts. In the first part, a software is designed that can perform cell viability analysis for use in an investigation targeting bone tissue regeneration. It is possible to examine the viabilities of the cells in the tissue scaffold depending on the depth of the scaffold using this software. The percentage of dead and live cell area can be calculated with the developed software. In order to test this software, a data set of 35 images was created. The generated data set includes cell culture images of alginate and alginate-hydroxyapatite tissue scaffolds visualized with laser scanning confocal microscopy (LSCM). Two different analyzes were performed with the designed algorithm. In the first analysis, the algorithm measurement results were compared with the ImageJ measurement results of four different analysts. It has been determined that the average of the analyst measurements is similar to the algorithm measurements. In addition, inter-analyst variabilities of measurements were examined by calculating the coefficient of variation (measurement deviation). The variation of coefficient values was determined from 13.18% to 98.34% for living cell images and from 9.75% to 126.02% for dead cell images. In the second analysis, the depth-dependent variation of cell viability of three different tissue scaffolds (alginate-HAp, conventional gel-MA and microwave-assisted gel-MA) was examined. As a result of this investigation, the maximum cell viability was determined at 63 μm, 72 μm and 90 μm depths for microwave-assisted gel-MA, alginate-HAp and conventional gel-MA scaffolds, respectively. In the second part of the study, a software was developed to examine the physical properties of tissue scaffolds composed of nanofiber or micro metal rod structures. In order to test this software, a data set of 46 images was created. The generated data set includes images of nanofibers and micro metal rod visualized by scanning electron microscopy (SEM). An adaptive approach was designed that includes three different segmentation methods for segmenting 46 images with different brightness and contrast properties. The designed algorithm has three different segmentation methods including clustering, local thresholding and histogram-dependent thresholding. The segmentation results of the designed software are compared with the segmentation results of open source code DiameterJ software. The results were compared with a function called structural similarity index measurement (SSIM). In the results of segmentation of 46 images, the designed algorithm obtained the best results for 27 images and DiameterJ software obtained the best results for 19 images. After the comparison process, the physical properties were analyzed in the segmented images of the tissue scaffolds. The pore structure properties (minimum, maximum pore size and total pore percentage) and the fiber structure properties (diameter range, diameter distribution, minimum and maximum diameter value) of each tissue scaffold were determined.