An introduction to digital image processing
An introduction to digital image processing
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
On the computational complexity of reconstructing lattice sets from their x-rays
Discrete Mathematics
Advances in Discrete Tomography and Its Applications (Applied and Numerical Harmonic Analysis)
Advances in Discrete Tomography and Its Applications (Applied and Numerical Harmonic Analysis)
Efficient computation of adaptive threshold surfaces for image binarization
Pattern Recognition
IBM Journal of Research and Development
Optimal threshold selection for tomogram segmentation by reprojection of the reconstructed image
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Threshold Selection for Segmentation of Dense Objects in Tomograms
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Adaptive thresholding of tomograms by projection distance minimization
Pattern Recognition
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Segmentation is an important step to obtain quantitative information from tomographic data sets. To this end, global thresholding is often used in practice. However, it is usually not possible to obtain an accurate segmentation based on a single, global threshold. Instead, local thresholding schemes can be applied that use a varying threshold, depending on local characteristics of the tomogram. Selecting the best local thresholds is not a straightforward task, as local image features often do not provide sufficient information for choosing a proper threshold. Recently, the concept of projection distance was proposed as a new criterion for evaluating the quality of a tomogram segmentation. In this paper, we describe how Projection Distance Minimization (PDM) can be used to select local thresholds, based on the available projection data from which the tomogram was initially computed. By reprojecting the segmented image, a comparison can be made with the measured projection data. This yields a quantitative measure of the quality of the segmentation. By minimizing the difference between the computed and measured projections, optimal local thresholds can be computed. Simulation experiments have been performed, comparing our local thresholding approach with an alternative local thresholding method and with optimal global thresholding. Our results demonstrate that the local thresholding approach yields segmentations that are significantly more accurate, in particular when the tomogram contains artifacts.