Publication detail

Significant Parameters of Image Reconstruction Convergence

OSTANINA, K. DĚDKOVÁ, J.

Original Title

Significant Parameters of Image Reconstruction Convergence

English Title

Significant Parameters of Image Reconstruction Convergence

Type

conference paper

Language

en

Original Abstract

Classical electrical impedance tomography (EIT) is an imaging modality in which the internal volume impedivity distribution is reconstructed based on the known injected currents and measured voltages on the surface of the object. Image reconstruction is an ill-posed inverse problem of finding such internal impedivity distribution that minimizes certain optimization criteria. The optimization necessitates algorithms that impose regularization and some prior information constraint. The regularization techniques vary in their complexity. This paper proposes the specification of significant parameters of regularization techniques such as the Tikhonov regularization method. We intend to show in the proposed paper the influence of these parameters on the stability, accuracy and convergence of an optimization process. The optimal parameters were found and applied during the image reconstruction process for a two-dimensional (2D) example. The obtained results were presented in related research reports.

English abstract

Classical electrical impedance tomography (EIT) is an imaging modality in which the internal volume impedivity distribution is reconstructed based on the known injected currents and measured voltages on the surface of the object. Image reconstruction is an ill-posed inverse problem of finding such internal impedivity distribution that minimizes certain optimization criteria. The optimization necessitates algorithms that impose regularization and some prior information constraint. The regularization techniques vary in their complexity. This paper proposes the specification of significant parameters of regularization techniques such as the Tikhonov regularization method. We intend to show in the proposed paper the influence of these parameters on the stability, accuracy and convergence of an optimization process. The optimal parameters were found and applied during the image reconstruction process for a two-dimensional (2D) example. The obtained results were presented in related research reports.

Keywords

image processing, EIT

RIV year

2010

Released

24.05.2010

ISBN

978-80-554-0196-6

Book

ELEKTRO 2010 proceedings

Pages from

41

Pages to

44

Pages count

4

BibTex


@inproceedings{BUT29160,
  author="Ksenia {Kořínková} and Jarmila {Dědková}",
  title="Significant Parameters of Image Reconstruction Convergence",
  annote="Classical electrical impedance tomography (EIT) is an imaging modality in which the internal volume impedivity distribution is reconstructed based on the known injected currents and measured voltages on the surface of the object. Image reconstruction is an ill-posed inverse problem of finding such internal impedivity distribution that minimizes certain optimization criteria. The optimization necessitates algorithms that impose regularization and some prior information constraint. The regularization techniques vary in their complexity. This paper proposes the specification of significant 
parameters of regularization techniques such as the Tikhonov regularization method. We intend to show in the proposed paper the influence of these parameters on the stability, accuracy and convergence of an optimization process. The optimal parameters were found and applied during the image reconstruction process for a two-dimensional (2D) example. The obtained results were presented in related research reports.",
  booktitle="ELEKTRO 2010 proceedings",
  chapter="29160",
  howpublished="print",
  year="2010",
  month="may",
  pages="41--44",
  type="conference paper"
}