Fused Multi-Characteristic Validity Index: An Application to Reconstructed Image Evaluation in Electrical Tomography

Shihong Yue, Teresa Wu, Zhiqing Liu, Xian Zhao

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

The quality of reconstructed images is an important and direct criterion to quantitatively evaluate the effectiveness of reconstruction algorithms in electrical tomography (ET). Unfortunately, there lacks of effective and efficient approach to assessing the quality of ET images in literature and practices. Realizing the gap, we recently develop a novel index termed fused Multiple Characteristic Indices (fMCI) 1 aiming to measure the quality of clustering various data sets including imaging data (e.g., ET). In this paper, we propose a method based on fMCI to quantitatively evaluate the quality of reconstructed images. The method first applies the fast fuzzy c-means algorithm to cluster pixels in the reconstruct image. The fMCI is then applied to evaluate the clustering results and image quality. Simulated data derived from finite element method is used to demonstrate that the proposed method is capable to evaluate the quality of the reconstructed images and the results are consistent with visual perception. In addition, a number of experiments are conducted to demonstrate the applicability and effectiveness of the fMCI based method for image quality assessment.

Original languageEnglish (US)
Pages (from-to)1052-1061
Number of pages10
JournalInternational Journal of Computational Intelligence Systems
Volume4
Issue number5
DOIs
StatePublished - Sep 1 2011

Keywords

  • Electrical tomography
  • Validity index
  • image evaluation
  • image reconstruction

ASJC Scopus subject areas

  • Computer Science(all)
  • Computational Mathematics

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