Abstract
Abstract We present a mesh generation algorithm which is able to produce smooth meshes from point clouds derived from histological slices. In this work, the shrinking tube mesh generation is used on histologic images depicting pathologic vessels. Our mesh generation is modeled after the behaviour of a shrinking tube. A start shape is fitted iteratively to the point cloud. The presented algorithm was successfully used to generate meshes of the inner and outer contour from vessels in histologic images. While histologic slices have a high in-plane resolution, the large slice distance and deformations during tissue deformations are challenging for 3D model generation.
Highlights
Mesh generation is especially challenging when working with histologic images
A point cloud is available consisting of points which are nearly evenly spaced over the object, for example from a 3D scan of an object
The time needed for the shrinking tube mesh generation increases with the influence area factor as shown in Figure 5 (Experiments were run on a computer with an Intel i7 processor with 16 GB RAM)
Summary
Mesh generation is especially challenging when working with histologic images. Often the meshes derived from point clouds require a post processing step to smooth the meshes [6]. Some algorithms require extensive preprocessing, for example the calculation of point normals [7]. This preprocessing is justified, the normals are error-prone in case of incorrect segmentations. In the images the inner and outer contour is segmented. Due to the high resolution of histolgic images, the reduction to contours and point clouds allows for a more efficient processing. The ball pivoting algorithm [9] only produces a few faces along the points of one slice and cannot handle the large distance between points from different slices
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