TY - JOUR
T1 - Shape based reconstruction of experimental data in 3D electrical capacitance tomography
AU - Banasiak, R
AU - Soleimani, M
PY - 2010
Y1 - 2010
N2 - Electrical Capacitance Tomography (ECT) is a non-invasive and non-destructive imaging technique that uses
electrical capacitance measurement at the periphery of an object. The image reconstruction problem in ECT is an
ill-posed inverse problem. This paper presents a level set based shape reconstruction method applied to 3D ECT
using experimental data. The finite element models have been implemented based on a 32 electrode ECT system
to formulate the forward problem. Development of the level set technique enables detection of smaller inclusions
and improves the accuracy of boundary shapes of inclusions. The paper uses a shape based method rather than
traditional image based methods. The shape-based approach offers several advantages compared to more
traditional voxel-based approaches. The incorporation of an intrinsic regularization in the form of a-priori
assumptions, regarding the general anatomical structures present in the medium, reduces the dimensionality of
the inverse problem and thereby stabilizes the reconstruction. The level set strategy (which is an implicit
representation of the shapes) can handle the topological during this reconstruction process. Additionally in this
paper a new two-stage level set method has been developed, which shows significant improvement compared
with the traditional level set reconstruction algorithm.
AB - Electrical Capacitance Tomography (ECT) is a non-invasive and non-destructive imaging technique that uses
electrical capacitance measurement at the periphery of an object. The image reconstruction problem in ECT is an
ill-posed inverse problem. This paper presents a level set based shape reconstruction method applied to 3D ECT
using experimental data. The finite element models have been implemented based on a 32 electrode ECT system
to formulate the forward problem. Development of the level set technique enables detection of smaller inclusions
and improves the accuracy of boundary shapes of inclusions. The paper uses a shape based method rather than
traditional image based methods. The shape-based approach offers several advantages compared to more
traditional voxel-based approaches. The incorporation of an intrinsic regularization in the form of a-priori
assumptions, regarding the general anatomical structures present in the medium, reduces the dimensionality of
the inverse problem and thereby stabilizes the reconstruction. The level set strategy (which is an implicit
representation of the shapes) can handle the topological during this reconstruction process. Additionally in this
paper a new two-stage level set method has been developed, which shows significant improvement compared
with the traditional level set reconstruction algorithm.
UR - http://www.scopus.com/inward/record.url?scp=76349102792&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1016/j.ndteint.2009.12.001
U2 - 10.1016/j.ndteint.2009.12.001
DO - 10.1016/j.ndteint.2009.12.001
M3 - Article
VL - 43
SP - 241
EP - 249
JO - NDT and E International
JF - NDT and E International
IS - 3
ER -