Optimal Threshold Selection for Segmentation of Dense Homogeneous Objects in Tomographic Reconstructions

In this paper, we present a novel approach to segment dense, homogeneous objects in a tomographic reconstruction (or tomogram). A popular method to extract such objects from a tomogram is global thresholding, in which the threshold value is determined from the image histogram. However, accurate threshold selection is not straightforward, since, due to noise or artefacts in the reconstruction, the histogram does not always contain a clear, separate peak for the dense object. We propose a new threshold estimation approach, segmentation inconsistency minimization, that exploits the available projection data to determine the optimal global threshold. The proposed algorithm was tested on simulation data and on experimental μCT data. The results show that this method results in more accurate segmentations, compared to alternative threshold selection methods.