Master Project: Based on Image processing using Matlab and image processing algorithms. Blend Altmann technique for analysis
CT-Scan images are used in clinical field to assess the patient health. In quantitative CT (QCT) parameters like volume, density, thickness, morphometric etc. of the bones can be estimated. Estimation of QCT parameters involve image processing steps like segmentation, registration and parametric analysis. Different CT-scanners and protocols affect image quality parameters like image resolution, noise, contrast, sharpness, slice thickness, grey level, image size and voxel size differently. Estimation of QCT parameters may change between CT-scanners, even for the same specimen. CT-scanners have different parameter settings from another like resolution, noise, contrast, pixel size, kernel, spatial frequency, number of projections, voltage, current, exposure etc. which can produce different results for same specimen. The aim of this project was to evaluate and quantify the differences in QCT for Cortical bone parameters when using different CT-scanners for manual segmentation compared to image registration methods. Methods of image processing are used to compare QCT parameters like cortical density and thickness between CT-scanners. Manual segmentation is time consuming but image processing methods are faster. This experiment was conducted to evaluate that which method performs better in terms of result, time and quality. Therefore, it can be used in future.
Data is not available because of patients consents.