Application of segmentation for correction of intensity bias in x-ray computed tomography images

Pavel Iassonov, Markus Tuller

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Nondestructive imaging methods such as x-ray computed tomography (CT) yield high-resolution, grayscale, threedimensional visualizations of pore structures and fluid interfaces in porous media. To separate solid and fluid phases for quantitative analysis and fluid dynamics modeling, segmentation is applied to convert grayscale CT volumes to discrete representations of media pore space. Unfortunately, x-ray CT is not free of artifacts, which complicates segmentation and quantitative image analysis due to obscuration of significant features or misinterpretation of att enuation values of a single material in different image sections. Images or volumes emanating from polychromatic (industrial) scanners are especially prone to high noise levels, beam hardening, scattered x-rays, or ring artifacts. These problems can be alleviated to a certain extent through application of metal filters, careful detector calibration, and sample centering, but they cannot be completely avoided. We have developed a simple three-dimensional approach to numerically correct for image artifacts using sequential segmentation. This procedure leads to a significant improvement of grayscale data as well as final segmentation results with reasonable computational demand.

Original languageEnglish (US)
Pages (from-to)187-191
Number of pages5
JournalVadose Zone Journal
Volume9
Issue number1
DOIs
StatePublished - Feb 2010

Fingerprint

computed tomography
segmentation
tomography
X-radiation
artifact
image analysis
scanners
porous media
imaging method
detectors
fluid
quantitative analysis
fluid dynamics
fluid mechanics
calibration
pore space
scanner
hardening
metals
visualization

ASJC Scopus subject areas

  • Soil Science

Cite this

Application of segmentation for correction of intensity bias in x-ray computed tomography images. / Iassonov, Pavel; Tuller, Markus.

In: Vadose Zone Journal, Vol. 9, No. 1, 02.2010, p. 187-191.

Research output: Contribution to journalArticle

@article{6e571d053eaf41a890252456d07e0b9f,
title = "Application of segmentation for correction of intensity bias in x-ray computed tomography images",
abstract = "Nondestructive imaging methods such as x-ray computed tomography (CT) yield high-resolution, grayscale, threedimensional visualizations of pore structures and fluid interfaces in porous media. To separate solid and fluid phases for quantitative analysis and fluid dynamics modeling, segmentation is applied to convert grayscale CT volumes to discrete representations of media pore space. Unfortunately, x-ray CT is not free of artifacts, which complicates segmentation and quantitative image analysis due to obscuration of significant features or misinterpretation of att enuation values of a single material in different image sections. Images or volumes emanating from polychromatic (industrial) scanners are especially prone to high noise levels, beam hardening, scattered x-rays, or ring artifacts. These problems can be alleviated to a certain extent through application of metal filters, careful detector calibration, and sample centering, but they cannot be completely avoided. We have developed a simple three-dimensional approach to numerically correct for image artifacts using sequential segmentation. This procedure leads to a significant improvement of grayscale data as well as final segmentation results with reasonable computational demand.",
author = "Pavel Iassonov and Markus Tuller",
year = "2010",
month = "2",
doi = "10.2136/vzj2009.0042",
language = "English (US)",
volume = "9",
pages = "187--191",
journal = "Vadose Zone Journal",
issn = "1539-1663",
publisher = "Soil Science Society of America",
number = "1",

}

TY - JOUR

T1 - Application of segmentation for correction of intensity bias in x-ray computed tomography images

AU - Iassonov, Pavel

AU - Tuller, Markus

PY - 2010/2

Y1 - 2010/2

N2 - Nondestructive imaging methods such as x-ray computed tomography (CT) yield high-resolution, grayscale, threedimensional visualizations of pore structures and fluid interfaces in porous media. To separate solid and fluid phases for quantitative analysis and fluid dynamics modeling, segmentation is applied to convert grayscale CT volumes to discrete representations of media pore space. Unfortunately, x-ray CT is not free of artifacts, which complicates segmentation and quantitative image analysis due to obscuration of significant features or misinterpretation of att enuation values of a single material in different image sections. Images or volumes emanating from polychromatic (industrial) scanners are especially prone to high noise levels, beam hardening, scattered x-rays, or ring artifacts. These problems can be alleviated to a certain extent through application of metal filters, careful detector calibration, and sample centering, but they cannot be completely avoided. We have developed a simple three-dimensional approach to numerically correct for image artifacts using sequential segmentation. This procedure leads to a significant improvement of grayscale data as well as final segmentation results with reasonable computational demand.

AB - Nondestructive imaging methods such as x-ray computed tomography (CT) yield high-resolution, grayscale, threedimensional visualizations of pore structures and fluid interfaces in porous media. To separate solid and fluid phases for quantitative analysis and fluid dynamics modeling, segmentation is applied to convert grayscale CT volumes to discrete representations of media pore space. Unfortunately, x-ray CT is not free of artifacts, which complicates segmentation and quantitative image analysis due to obscuration of significant features or misinterpretation of att enuation values of a single material in different image sections. Images or volumes emanating from polychromatic (industrial) scanners are especially prone to high noise levels, beam hardening, scattered x-rays, or ring artifacts. These problems can be alleviated to a certain extent through application of metal filters, careful detector calibration, and sample centering, but they cannot be completely avoided. We have developed a simple three-dimensional approach to numerically correct for image artifacts using sequential segmentation. This procedure leads to a significant improvement of grayscale data as well as final segmentation results with reasonable computational demand.

UR - http://www.scopus.com/inward/record.url?scp=77349095023&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77349095023&partnerID=8YFLogxK

U2 - 10.2136/vzj2009.0042

DO - 10.2136/vzj2009.0042

M3 - Article

AN - SCOPUS:77349095023

VL - 9

SP - 187

EP - 191

JO - Vadose Zone Journal

JF - Vadose Zone Journal

SN - 1539-1663

IS - 1

ER -