EIT image regularization by a new Multi-Objective Simulated Annealing algorithm

Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image recon...

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Veröffentlicht in:2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Jg. 2015; S. 4069 - 4072
Hauptverfasser: Martins, Thiago Castro, Sales Guerra Tsuzuki, Marcos
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2015
Schlagworte:
Algorithms
Clustering algorithms
Electric Impedance
Energy states
Image Processing, Computer-Assisted - methods
Image reconstruction
Linear programming
Simulated annealing
Tomography
Tomography - methods
ISSN:1094-687X, 1557-170X, 2694-0604, 2694-0604
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Abstract Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.
AbstractList Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.
Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.
Author Sales Guerra Tsuzuki, Marcos
Martins, Thiago Castro
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  surname: Sales Guerra Tsuzuki
  fullname: Sales Guerra Tsuzuki, Marcos
  email: mtsuzuki@usp.br
  organization: Escola Politécnica da Universidade de São Paulo, Brazil
BackLink https://www.ncbi.nlm.nih.gov/pubmed/26737188$$D View this record in MEDLINE/PubMed
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Snippet Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not...
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SubjectTerms Algorithms
Clustering algorithms
Electric Impedance
Energy states
Image Processing, Computer-Assisted - methods
Image reconstruction
Linear programming
Simulated annealing
Tomography
Tomography - methods
Title EIT image regularization by a new Multi-Objective Simulated Annealing algorithm
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https://www.ncbi.nlm.nih.gov/pubmed/26737188
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