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Application of expectation maximization algorithm in magnetic induction tomography

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Abstract

Purpose

The aim of this paper is to investigate the image reconstruction problem by using expectation maximization (EM) algorithm to enhance the quality of magnetic induction tomography (MIT).

Methods

The EM algorithm is used to solve the image reconstruction problem. It is based on the maximum likelihood criterion, and the voltage data and the sensitivity matrix are considered as the incomplete-data. Within the incompletedata framework of the EM algorithm, the image reconstruction can be converted to the problem of EM through the likelihood function.

Results

Simulation experiments are conducted and show that the proposed method has better quality of image than the Tikhonov regulation method and iteration Newton-Raphson (INR) algorithm have.

Conclusions

From the experimental results and comparative studies, we can infer the scheme applied in MIT can improve the quality of image.

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Authors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116023, China

    Min Han & Yuyan Xue

Authors
  1. Min Han
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  2. Yuyan Xue
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Correspondence to Min Han.

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Han, M., Xue, Y. Application of expectation maximization algorithm in magnetic induction tomography. Biomed. Eng. Lett. 5, 221–228 (2015). https://doi.org/10.1007/s13534-015-0192-0

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  • DOI: https://doi.org/10.1007/s13534-015-0192-0

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