Discretized Target Size Detection in Electrical Impedance Tomography Using Neural Network Classifier

Shu-Wei Huang, Gustavo K. Rohde, Hao-Min Cheng, Shien-Fong Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Electrical impedance tomography (EIT) uses non-invasive and non-radiative imaging to detect inhomogeneous electrical properties in tissues. The inverse problem of EIT is a highly nonlinear, ill-posed problem, which causes inaccuracy in target size calculation. We propose a novel approach to discretize the target size and use a neural network (NN) classifier to classify the unknown size in discrete steps. The target size is discretized into distinct steps, and each step can be a unique class. The data is pre-processed with the cumulative distribution transform (CDT) to enhance distinguishability. First, the NN is trained with simulated datasets, divided into time difference (t-d) group and CDT group. After training, the NN classifier is tested by experimental data recorded in a phantom experiment. Linear discriminant analysis (LDA) is performed to assess the distinguishability of classes. There is a significant increase in distance between classes after the CDT pre-processing. The density of the classes has an upward trend with a higher degree of clustering after CDT pre-processing. The CDT data clustering into distinguishable classes is essential to excellent NN classification results. Such an approach is a significant paradigm shift by turning the cumbersome inverse calculation with uncertain accuracy into a classification problem with predetermined step errors. The accuracy and resolution can be further extended by increasing the discretization steps.

Original languageEnglish
Article number79
Number of pages9
JournalJournal of Nondestructive Evaluation
Issue number4
StatePublished - 19 Oct 2020


  • Electrical impedance tomography
  • Neural network classifier
  • Cumulative distribution transform

Cite this