Title
Automatic local thresholding of tomographic reconstructions based on the projection dataAutomatic local thresholding of tomographic reconstructions based on the projection data
Author
Faculty/Department
Faculty of Sciences. Physics
Research group
Vision lab
Publication type
conferenceObject
Publication
s.l. , [*]
Subject
Physics
Source (book)
Proceedings of SPIE Medical Imaging, San Diego, USA, February 2008
ISSN
0277-786X
ISBN
978-0-8194-7097-3
ISI
000256660300092
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Tomography is an important technique for non-invasive imaging, with applications in medicine, materials research and industry. Tomographic reconstructions are typically gray-scale images, that can possibly contain a wide spectrum of grey levels. Segmentation of these grey level images is an important step to obtain quantitative information from tomographic datasets. Thresholding schemes are often used in practice, as they are easy to implement and use. However, if the tomogram exhibits variations in the intensity throughout the image, it is not possible to obtain an accurate segmentation using 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 (such as the local histogram) often do not provide sufficient information for choosing a proper threshold. In this paper, we propose a new criterion for selecting 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 the result of our local thresholding approach with global thresholding. Our results demonstrate that the local thresholding approach yields segmentations that are significantly more accurate, in particular when the tomogram contains artifacts.
E-info
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000256660300092&DestLinkType=RelatedRecords&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000256660300092&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
Handle