China
Africa
Explore chapters and articles related to this topic
Mathematical Morphology Methods
Published in Yu-Jin Zhang, A Selection of Image Analysis Techniques, 2023
The skeleton is obtained by refining and simplifying a region, and has an equivalent representation to the region (the region can be reconstructed from the skeleton). Extracting the skeleton of an object is an important way to represent the shape and structure of the object, and it is often referred to as the skeletonization of the object. Mathematical morphological methods can be used to calculate the skeleton. Let S(A) represent the skeleton of A, it can be represented as: S(A)=∪k=0KSk(A)
Experimental Design, Data Analysis, Visualization
Published in Stuart R. Stock, MicroComputed Tomography, 2018
Skeletonization refers to a representation of an object by its centerline, that is, by reducing the object to a one-voxel-wide string or branched string extending to the extreme ends of the original object. This representation of data simplifies analysis of complex arrays of objects, particularly those that are channel- or fiberlike. Skeletonization analysis was used, for example, to study two bonded stainless steel fiber assemblies and to show that the distribution of fiber segment lengths between the two specimens differed, as did the distribution of fiber orientations (Tan et al., 2006). Such orientation data are often shown on stereographic projections; see Cullity and Stock (2001) or other materials texts for an introduction to this type of plot.
Experimental Design, Data Analysis, Visualization
Published in Stuart R. Stock, MicroComputed Tomography, 2019
Skeletonization refers to a representation of an object by its centerline, that is, by reducing the object to a one-voxel-wide string or branched string extending to the extreme ends of the original object. This representation of data simplifies analysis of complex arrays of objects, particularly those that are channel- or fiber-like. Skeletonization analysis was used, for example, to study two bonded stainless steel fiber assemblies and to show that the distribution of fiber segment lengths between the two specimens differed as did the distribution of fiber orientations (Tan, Elliott et al. 2006). Such orientation data are often shown on stereographic projections; see Cullity and Stock (2001) or other materials texts for an introduction to this type of plot.
Automatic 3-D tubular centerline tracking of coronary arteries in coronary computed tomographic angiography
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2018
Nasrin Salehi, Ahmad Reza Naghsh-Nilchi
After applying MHT algorithm, we have a chain of tracked points as initial tree without any branches. The presented algorithm tracks the branches of the previously tracked centerline in two depth branch searching steps. For the first step, let's define a set as the set of nine points including any point on the centerline, q, and four subsequent tracked points as well as four previous tracked points of q. Search for branches of the centerline is done by forming the set , starting from the earliest acquired centerline points. We sort the points in the set according to slice number. Then, a seeded 2D region growing algorithm is applied starting from current tracked point q in a rectangular region around it. Next, a 2D morphological skeletonisation is applied to find branches and more generally vessel structures within this rectangular region. By applying skeletonisation, branches and narrowed parts of the vessels in which MHT algorithm may lose the track of the vessel can be easily recognised. Here, we refer skeletonisation as a thinning algorithm that reduces lines to single pixel thickness lines.
Casualty Identification with Dental Radiographs and Photographs
Published in IETE Journal of Research, 2023
B. Vijayakumari, M. Vasanthal, S. Dhivya Dharshini
Skeletonization is a process for reducing foreground regions in the binary image to a skeletal remnant that largely preserves the extent and connectivity of the original region while throwing away most of the original foreground pixels. Skeleton-based matching is preferable because an intuitive representation of shape is possible, provided allowing the user to have more control in the matching process.