Endpoint Detection of Partially Overlapping Straight Fibers using High Positive Gaussian Curvature in 3D images

Markus Kronenberger, Katja Schladitz, Oliver Wirjadi, Christopher Weber, Bernd Hamann, Hans Hagen

Abstract

This paper introduces a method for detecting endpoints of partially overlapping straight fibers in three-dimensional voxel image data. The novel approach directly determines fiber endpoints without the need for more expansive single-fiber segmentation. In the context of fiber-reinforced polymers, endpoint information is of practical significance as it can indicate potential damage in endless fiber systems, or can serve as input for estimating statistical fiber length distribution. We tackle this challenge by exploiting Gaussian curvature of the surface of the fibers. Fiber endpoints have high positive curvature, allowing one to distinguish them from the rest of a structure. Accuracy data of the proposed method are presented for various data sets. For simulated fiber systems with fiber volume fractions of less than 20 %, true positive rates above 94 % and false positive rates below 5 % are observed. Two well-resolved real data sets show a reduction of the first rate to 90.3 % and an increase of the second rate to 13.1 %.


Keywords
endpoint segmentation; image processing; X-ray micro-computed tomography; fiber reinforced polymer

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DOI: 10.5566/ias.2197

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Image Analysis & Stereology
EISSN 1854-5165 (Electronic version)
ISSN 1580-3139 (Printed version)