TY - JOUR
AU - Nikolaev, Dmitry
AB - X-ray computed tomography provides information about the internal structure of the object under study. Standardizing the orientation of the reconstructed volume is a critical stage in many volumetric data processing and analysis pipelines that require a certain (strictly specified) orientation of the reconstructed volume. Since microelectronic devices are mostly planar in structure, virtual two-dimensional cross sections of the reconstructed volume must be located along planar layers. However, it is not always possible to accurately orient the object to be scanned, so post-processing algorithms are used for automatic alignment. In some tasks, the strict orientation of the digital image of a 3d object is a necessary condition for the operation of post-processing algorithms. An example of a post-processing algorithm with stringent object orientation requirements is the algorithm for automatically unfolding a digital copy of a collapsed object (scroll). Orientation requirements also arise when solving segmentation problems, searching for special points, stitching parts of the image to improve the quality of the algorithm result. For learning methods such as artificial neural networks, using standardized volume orientation can dramatically reduce the variability of the data, which allows for achieving high performance with less computational overhead and data preparation. In this paper, we proposed an automatic orthotropic alignment method to achieve the desired object orientation. The algorithm implements the optimization of the orientation model parameters using the RANSAC method. The numerical implementation of the method is done in Python language. The performance of the method is demonstrated on three datasets: a digital model of a test 3d object, a flash drive tomographic reconstruction result, and a scroll tomographic reconstruction result. For the same objects, calculations were carried out using previously used methods based on the inertia tensor and structural tensor. The comparison of the obtained results showed that our proposed method is the most robust compared to the methods using the inertia tensor and the structural tensor. A detailed description of the comparison methodology is also presented in the paper. In medical research, all objects belong to one class and have a similar internal structure, so anatomical features are used to standardize orientation. In industrial and laboratory computed tomography, the variability of objects is much higher, so more general features, such as the presence of dedicated orientations, must be used. To our knowledge, this study is the first dedicated to the problem of automatic geometric normalization of tomographic reconstruction results of objects with orthotropic features. In this study, we propose and compare three different orthotropic alignment methods: inertia tensor-based, structural tensor-based, and RANSAC-based. Our experiments on synthetic data and real object reconstructions show the advantages of the RANSAC-based approach for automatic orthotropic alignment of reconstructed volumes.
TI - Orthotropic alignment for x-ray computed tomography images
JF - Proceedings of SPIE
DO - 10.1117/12.3056308
DA - 2025-02-10
UR - https://www.deepdyve.com/lp/spie/orthotropic-alignment-for-x-ray-computed-tomography-images-2rNN2fmYJ0
SP - 135400E
EP - 135400E-6
VL - 13540
IS - 
DP - DeepDyve
ER -