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 Citations (Scopus)

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 2011

Fingerprint

Validity Index
Tomography
Image quality
Evaluation
Evaluate
Image Quality Assessment
Fuzzy C-means Algorithm
Pixels
Visual Perception
Data Clustering
Imaging techniques
Finite element method
Reconstruction Algorithm
Image Quality
Demonstrate
Pixel
Finite Element Method
Imaging
Clustering
Experiments

Keywords

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

ASJC Scopus subject areas

  • Computational Mathematics
  • Computer Science(all)

Cite this

Fused Multi-Characteristic Validity Index : An Application to Reconstructed Image Evaluation in Electrical Tomography. / Yue, Shihong; Wu, Teresa; Liu, Zhiqing; Zhao, Xian.

In: International Journal of Computational Intelligence Systems, Vol. 4, No. 5, 09.2011, p. 1052-1061.

Research output: Contribution to journalArticle

@article{d44f8f995f884d8386bef7abed7ba391,
title = "Fused Multi-Characteristic Validity Index: An Application to Reconstructed Image Evaluation in Electrical Tomography",
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.",
keywords = "Electrical tomography, image evaluation, image reconstruction, Validity index",
author = "Shihong Yue and Teresa Wu and Zhiqing Liu and Xian Zhao",
year = "2011",
month = "9",
doi = "10.1080/18756891.2011.9727853",
language = "English (US)",
volume = "4",
pages = "1052--1061",
journal = "International Journal of Computational Intelligence Systems",
issn = "1875-6891",
publisher = "Atlantis Press",
number = "5",

}

TY - JOUR

T1 - Fused Multi-Characteristic Validity Index

T2 - An Application to Reconstructed Image Evaluation in Electrical Tomography

AU - Yue, Shihong

AU - Wu, Teresa

AU - Liu, Zhiqing

AU - Zhao, Xian

PY - 2011/9

Y1 - 2011/9

N2 - 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.

AB - 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.

KW - Electrical tomography

KW - image evaluation

KW - image reconstruction

KW - Validity index

UR - http://www.scopus.com/inward/record.url?scp=84860858211&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860858211&partnerID=8YFLogxK

U2 - 10.1080/18756891.2011.9727853

DO - 10.1080/18756891.2011.9727853

M3 - Article

AN - SCOPUS:84860858211

VL - 4

SP - 1052

EP - 1061

JO - International Journal of Computational Intelligence Systems

JF - International Journal of Computational Intelligence Systems

SN - 1875-6891

IS - 5

ER -