Image processing has become a tremendous tool for various fields of applications as well as for textile manufacturing industry in recent years. Inspection of fabric density is one of the major issues for fabric manufacturers in textile industries. In this study, an image processing method comprising of linear and nonlinear techniques for automatic inspection of warp and weft yarn density of fabrics has been proposed. By avoiding common problems of linear filtering such as blurring and localization, anisotropic diffusion filtering has been applied as preprocessing operation to enhance the edge region/boundaries between adjacent yarns of the fabric images. We conjecture that given a skewed gray level image, the number of peaks in the gray line profile of the image is minimized if the image is rotated in such a way that the inter-spaces between yarns are aligned with the vertical axis. Gabor filter, an orientation-sensitive filter, is applied to the skewed image at that angle to boost the edges between inter-spaces. The number of warp and weft yarn density has been inspected by applying gray line profile method. Simulations have been done on a wide range of fabric image data set. The results have shown that nonlinear and steered filters made a contribution to the performance of the method. The number warp and weft yarn densities are determined with an accuracy rates above 90%.

Image compression has become an inevitable tool along with the advancing medical data acquisition and telemedicine systems. The run-length encoding (RLE), one of the most effective and practical lossless compression techniques, is widely used in two-dimensional space with common scanning forms such as zigzag and linear. In this study, an algorithm which takes advantage of the potential simplicity of the run-length algorithm is devised in a volumetric approach for three-dimensional (3D) binary medical data. The proposed algorithm, namely 3D-RLE, being different from the two-dimensional approach that utilizes only intra-slice correlations, is designed to compress binary volumetric data by employing also the inter-slice correlation between the voxels. Furthermore, it is extended to several scanning forms such as Hilbert and perimeter to determine an optimal scanning procedure coherent with the morphology of the segmented organ in data. The algorithm is employed on four datasets for a comprehensive assessment. Numerical simulation results demonstrated that the performance of the algorithm is 1:30 better than those of the state-of-the-art techniques, on average.

The lower limit of the optimal code length for a compressed data is determined by the entropy of the data. In this context, mapping the data to a space having lower entropy by using a suitable predictor provides a significant contribution to the performance of the compression. In this study, it was aimed to reduce the entropy of medical images by using linear volumetric predictors which are utilizing inter-frame correlations. In this way, the compression lower bound is reduced and the source coding algorithms achieve higher performance. Within this study, two and three-dimensional predictors were applied to two data sets, computerized tomography images and natural images. Simulations have shown that volumetric predictors provide much higher compression ratios as they reduce entropy more than two-dimensional estimators using only intra-frame correlations.