Optimal threshold selection for tomogram segmentation by reprojection of the reconstructed image

Authors:
K. Joost Batenburg;Jan Sijbers
Affiliations:
University of Antwerp, Vision Lab, Wilrijk, Belgium;University of Antwerp, Vision Lab, Wilrijk, Belgium
Venue:
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Year:
2007

Citing 6
Cited 4

An analysis of histogram-based thresholding algorithms

CVGIP: Graphical Models and Image Processing
Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy

Pattern Recognition Letters
Automatic thresholding for defect detection

Pattern Recognition Letters
On minimum variance thresholding

Pattern Recognition Letters
Thresholding based on variance and intensity contrast

Pattern Recognition
Improved fully automatic liver segmentation using histogram tail threshold algorithms

ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III

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
The minimal entropy prior for simultaneous reconstruction and segmentation of in VIVO microct trabecular bone images

ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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

Quantified Score

Hi-index	0.00

Visualization

Abstract

Grey value thresholding is a segmentation technique commonly applied to tomographic image reconstructions. Many procedures have been proposed to optimally select the grey value thresholds based on the image histogram. In this paper, a new method is presented that uses the tomographic projection data to determine optimal thresholds. The experimental results for phantom images show that our method obtains superior results compared to established histogram-based methods.