Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.

Description and classification of soil structure using distance transform data

Abstract: Summary Classification of soil based on structure is useful for conveying information about physical properties and soil processes. The distance transform is an image analysis technique suitable for quantifying soil structure. An analysis of distance transform data, in the form of cumulative area distribution curves for previously published images of soil structures of various types, is presented. The images were used to derive a quantitative classification of structure using maximum distance of solid from a macropore (Dmax, measured), the distance from macropore space containing 50% of the solid area (k, derived by fitting a sigmoidal function to the cumulative area distribution curve), the total interface length between pore and solid per area of sample (IA, measured), the porosity or the proportion of pores per area of sample (PA, measured) and the pore distribution characteristic (n, derived by fitting a sigmoidal function to the cumulative area distribution curve) which is related to the number, continuity and distribution of pores. The influence of image resolution was investigated, and within limits found to be fairly small. The final classification of soil structure was based on the hypothesized relations between the descriptors and structure-forming processes.

High-Throughput Segmentation of Tiled Biological Structures using Random-Walk Distance Transforms.

Abstract: Various 3D imaging techniques are routinely used to examine biological materials, the results of which are usually a stack of grayscale images. In order to quantify structural aspects of the biological materials, however, they must first be extracted from the dataset in a process called segmentation. If the individual structures to be extracted are in contact or very close to each other, distance-based segmentation methods utilizing the Euclidean distance transform are commonly employed. Major disadvantages of the Euclidean distance transform, however, are its susceptibility to noise (very common in biological data), which often leads to incorrect segmentations (i.e. poor separation of objects of interest), and its limitation of being only effective for roundish objects. In the present work, we propose an alternative distance transform method, the random-walk distance transform, and demonstrate its effectiveness in high-throughput segmentation of three microCT datasets of biological tilings (i.e. structures composed of a large number of similar repeating units). In contrast to the Euclidean distance transform, this random-walk approach represents the global, rather than the local, geometric character of the objects to be segmented and, thus, is less susceptible to noise. In addition, it is directly applicable to structures with anisotropic shape characteristics. Using three case studies—stingray tessellated cartilage, starfish dermal endoskeleton, and the prismatic layer of bivalve mollusc shell—we provide a typical workflow for the segmentation of tiled structures, describe core image processing concepts that are underused in biological research, and show that for each study system, large amounts of biologically-relevant data can be rapidly segmented, visualized and analyzed.

Cell image segmentation method and a nuclear-to-cytoplasmic ratio evaluation method using the same

Abstract: A cell image segmentation method includes receiving a cell image, performing a nuclei initialization step to find an internal marker and an external marker to obtain a potential nuclei and a potential cell boundary, calculating a gradient map of the received cell image, performing a filtering step on the gradient map to generate a filtered gradient map, performing a nuclei detection step to obtain a segmented nuclei, and performing a nuclei validation step to obtain a valid nuclei. The nuclei initialization step includes performing a blob detection step to obtain a nuclei candidate, an outlier removal step to obtain the internal marker, a distance transform step to obtain a distance map, and a cell boundary initialization step to obtain the external marker. In another embodiment, a nuclear-to-cytoplasmic ratio evaluation method using the above cell image segmentation method is proposed.

Distance estimation method for a moving object having a constrained vertical path profile

Abstract: This method makes it possible to plot, from a terrain elevation database, a map of the distances of the points accessible to a moving object subject to constraints (inaccessible reliefs, unnegotiable obstacles, weather disturbances, path with an imposed vertical profile, etc.), the distances being measured only along paths that are practical for the moving object. It employs a chamfer distance transform applied to the image consisting of the projection on the horizontal plane of a 3D representation of the flying space of the moving object, which is likened to a mesh of elementary cubes associated with specific negotiation danger levels. It lists the typical paths without exceeding an acceptable danger threshold, going from a target point, the distance of which is to be estimated, to a source point, the origin of the distance measurements, and likens the distance of the target point to the length of the shortest practicable path or paths.

Method for reconstructing a distance field of a swept volume at a sample point

Abstract: A method performed on a processor reconstructs a distance field of an object at a sample point, where the object is a swept volume generated by moving a shape along a path. The shape is represented by a shape distance field. The path is represented by a parametric function. Distance data at the sample points is determined, where the distance data characterizes the distance field of the object at the sample point. An optimal set of parameters defining an optimal placement of the shape along the path is determined in a continuous manner. The shape distance field is transformed to the optimal placement to produce a transformed shape distance field. The distance data is determined at the sample point from the transformed shape distance field to reconstruct the distance field at the sample point.