Threshold Selection for Segmentation of Dense Objects in Tomograms

Authors:
W. Aarle;K. J. Batenburg;J. Sijbers
Affiliations:
IBBT - Vision Lab, University of Antwerp, Belgium;IBBT - Vision Lab, University of Antwerp, Belgium;IBBT - Vision Lab, University of Antwerp, Belgium
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Year:
2008

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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.