Abstract
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.
This work was financially supported by the IBBT, Flanders.
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van Aarle, W., Batenburg, K.J., Sijbers, J. (2008). Threshold Selection for Segmentation of Dense Objects in Tomograms. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_67
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DOI: https://doi.org/10.1007/978-3-540-89639-5_67
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