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2008 | OriginalPaper | Chapter
Threshold Selection for Segmentation of Dense Objects in Tomograms
Authors : W. van Aarle, K. J. Batenburg, J. Sijbers
Published in: Advances in Visual Computing
Publisher: Springer Berlin Heidelberg
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Tomographic reconstructions are often segmented to extract valuable quantitative information. In this paper, we consider the problem of segmenting a dense object of constant density within a continuous tomogram, by means of global thresholding. Selecting the proper threshold is a nontrivial problem, for which hardly any automatic procedures exists. We propose a new method that exploits the available projection data to accurately determine the optimal global threshold. Results from simulation experiments show that our algorithm is capable of finding a threshold that is close to the optimal threshold value.