Publication
Title
Optimal threshold selection for segmentation of dense homogeneous objects in tomographic reconstructions
Author
Abstract
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 (SICM), that exploits the available projection data to determine the optimal global threshold. The proposed algorithm was tested on simulation data and on experimental CT data. The results show that this method results in more accurate segmentations, compared to alternative threshold selection methods.
Language
English
Source (journal)
IEEE transactions on medical imaging / Institute of Electrical and Electronics Engineers [New York, N.Y.] - New York, N.Y.
Publication
New York, N.Y. : 2011
ISSN
0278-0062
Volume/pages
30:4(2011), p. 980-989
ISI
000289204000008
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 09.02.2011
Last edited 08.04.2017
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