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. © Springer-Verlag Berlin Heidelberg 2008.
CITATION STYLE
Aarle, W. V., Batenburg, K. J., & Sijbers, J. (2008). Threshold selection for segmentation of dense objects in tomograms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5358 LNCS, pp. 700–709). https://doi.org/10.1007/978-3-540-89639-5_67
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