Directional 3D Thinning Using 8 Subiterations

Abstract

Thinning of a binary object is an iterative layer by layer erosion to extract an approximation to its skeleton. In order to provide topology preservation, different thinning techniques have been proposed. One of them is the directional (or border sequential) approach in which each iteration step is subdivided into subiterations where only border points of certain kind are deleted in each subiteration. There are six kinds of border points in 3D images, therefore, 6-subiteration parallel thinning algorithms were generally proposed. In this paper, we present two 8-subiteration algorithms for extracting "surface skeletons" and "curve skeletons", respectively. Both algorithms work in cubic grid for (26,6) images. Deletable points are given by templates that makes easy implementation possible.

Surface and Curve Skeletonization of Large 3D Models on the GPU

“…Voxel-based methods include thinning, distance-field, and general-field methods. Thinning removes ∂ Ω voxels (or pixels in 2D) while preserving connectivity [7], [48]. Voxel removal in distance-to-boundary order enforces centeredness [53].…”

Skeletonization by blocks for large 3D datasets: application to brain microcirculation

“…The deletable points (simple points 1 that are also border points and non-end points 2 ) are removed either sequentially [7] or in parallel [8,9,10,11,12], or with morphological operations [13]. These methods lead to a skeleton homotopic to the object, by construction, thin and geometrically representative (if the endpoints have been correctly characterized), but not necessarily centered.…”

A Dense Medial Descriptor for Image Analysis

“…Geometric methods use a polygonized version of ∂Ω to compute its Voronoi diagram and the skeleton as a subset thereof (Ogniewicz and Kubler, 1995). Thinning methods iteratively remove ∂Ω pixels while preserving connectivity (Palagyi and Kuba, 1999).…”