Image Reconstruction for Electrical Impedance Tomography Using Enhanced Adaptive Group Sparsity with Total Variation

Yunjie Yang, Hancong Wu, Jiabin Jia

Research output: Contribution to journalArticlepeer-review

Abstract

A novel image reconstruction algorithm for electrical impedance tomography using enhanced adaptive group sparsity with total variation constraint is proposed in this paper. The new algorithm simultaneously utilizes the prior knowledge of regional structure feature and global characteristic of the conductivity distribution. The regional structure feature is encoded by using an enhanced adaptive group sparsity constraint. Meanwhile, the global characteristic of inclusion boundary is considered by imposing total variation constraint on the whole image. An enhanced adaptive pixel grouping algorithm is proposed based on Otsu's thresholding method, which demonstrates good noise immunity. An accelerated alternating direction method of multipliers is utilized to solve the proposed problem for a faster convergence rate. The performance of the proposed algorithm is thoroughly evaluated by numerical simulation and experiments. Comparing with the state-of-the-art algorithms, such as the L1 regularization, total variation regularization, and our former work on adaptive group sparsity, the proposed method has demonstrated superior spatial resolution and better noise reduction performance. Combined with the total variation constraint, distinct boundary of inclusions has also been obtained.
Original languageEnglish
JournalIEEE Sensors Journal
Volume17
Issue number17
Early online date17 Jul 2017
DOIs
Publication statusPublished - 1 Sep 2017

Keywords

  • Electrical Impedance Tomography
  • Enhanced adaptive group sparsity
  • Image reconstruction
  • Total variation

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