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
A Network Flow Algorithm for Reconstructing Binary Images from Continuous X-rays
Journal of Mathematical Imaging and Vision
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
Selection of local thresholds for tomogram segmentation by projection distance minimization
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Advanced cancer cell characterization and quantification of microscopy images
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Hi-index | 0.01 |
Segmentation is an important step to obtain quantitative information from tomographic data sets. 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. 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 by the authors as a new criterion for evaluating the quality of a tomogram segmentation [K.J. Batenburg, J. Sijbers, Automatic threshold selection for tomogram segmentation by reprojection of the reconstructed image, in: Computer Analysis of Images and Patterns, in: Lecture Notes in Computer Science, vol. 4673, Springer, Berlin/Heidelberg, 2007, pp. 563-570.]. 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. The results of several experiments are presented in which our local thresholding approach is compared with alternative thresholding methods. These results demonstrate that the local thresholding approach yields segmentations that are significantly more accurate compared to previously published methods, in particular when the initial reconstruction contains artifacts.