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Computing contrast ratio in medical images using local content information

Benhur Ortiz Jaramillo UGent, Asli Kumcu UGent, Ljiljana Platisa UGent and Wilfried Philips UGent (2015) Proceedings of The XVI Medical Image Perception Conference. p.34-34
abstract
Rationale Image quality assessment in medical applications is often based on quantifying the visibility between a structure of interest such as a vessel, termed foreground (F) and its surrounding anatomical background (B), i.e., the contrast ratio. A high quality image is the one that is able to make diagnostically relevant details distinguishable from the background. Therefore, the computation of contrast ratio is an important task in automatic medical image quality assessment. Methods We estimate the contrast ratio by using Weber’s law in local image patches. A small image patch can contain a flat area, a textured area or an edge. Regions with edges are characterized by bimodal histograms representing B and F, and the local contrast ratio can be estimated using the ratio between mean intensity values of each mode of the histogram. B and F are identified by computing the mid-value between the modes using the ISODATA algorithm. This process is performed over the entire image with a sliding window resulting in a contrast ratio per pixel. Results We have tested our measure on two general purpose databases (TID2013 [1] and CSIQ [2]) to demonstrate that the proposed measure agrees with human preferences of quality. Since our measure is specifically designed for measuring contrast, only images exhibiting contrast changes are used. The difference between the maximum of the contrast ratios corresponding to the reference and processed images is used as a quality predictor. Human quality scores and our proposed measure are compared with the Pearson correlation coefficient. Our experimental results show that our method is able to accurately predict changes of perceived quality due to contrast decrements (Pearson correlations higher than 90%). Additionally, this method can detect changes in contrast level in interventional x-ray images acquired with varying dose [3]. For instance, the resulting contrast maps demonstrate reduced contrast ratios for vessel edges on X-ray images acquired at lower dose settings, i.e., lower distinguishability from the background, compared to higher dose acquisitions. Conclusions We propose a measure to compute contrast ratio by using Weber’s law in local image patches. While the proposed contrast ratio is computationally simple, this approximation of local content has shown to be useful in measuring quality differences due to contrast decrements in images. Especially, changes in structures of interest due to low contrast ratio can be detected by using the contrast map making our method potentially useful in Xray imaging dose control. References [1] Ponomarenko N. et al., “A New Color Image Database TID2013: Innovations and Results,” Proceedings of ACIVS, 402-413 (2013). [2] Larson E. and Chandler D., "Most apparent distortion: full-reference image quality assessment and the role of strategy," Journal of Electronic Imaging, 19 (1), 2010. [3] Kumcu, A. et al., “Interventional x-ray image quality measure based on a psychovisual detectability model,” MIPS XVI, Ghent, Belgium, 2015.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
Weber's law, Contrast ratio, local content information, image quality
in
Proceedings of The XVI Medical Image Perception Conference
pages
34 - 34
publisher
MIPS conference website
place of publication
http://mips.ws/
conference name
The Medical Image Perception Conference
conference location
Ghent, Belgium
conference start
2015-06-02
conference end
2015-06-05
project
Ultra wide context aware imaging
language
English
UGent publication?
yes
classification
C3
copyright statement
I have retained and own the full copyright for this publication
id
5974740
handle
http://hdl.handle.net/1854/LU-5974740
date created
2015-06-04 02:01:56
date last changed
2017-01-02 09:53:25
@inproceedings{5974740,
  abstract     = {Rationale
Image quality assessment in medical applications is often based on quantifying the visibility between a structure
of interest such as a vessel, termed foreground (F) and its surrounding anatomical background (B), i.e., the
contrast ratio. A high quality image is the one that is able to make diagnostically relevant details distinguishable
from the background. Therefore, the computation of contrast ratio is an important task in automatic medical
image quality assessment.
Methods
We estimate the contrast ratio by using Weber{\textquoteright}s law in local image patches. A small image patch can contain a
flat area, a textured area or an edge. Regions with edges are characterized by bimodal histograms representing B
and F, and the local contrast ratio can be estimated using the ratio between mean intensity values of each mode
of the histogram. B and F are identified by computing the mid-value between the modes using the ISODATA
algorithm. This process is performed over the entire image with a sliding window resulting in a contrast ratio per
pixel.

Results
We have tested our measure on two general purpose databases (TID2013 [1] and CSIQ [2]) to demonstrate that
the proposed measure agrees with human preferences of quality. Since our measure is specifically designed for
measuring contrast, only images exhibiting contrast changes are used. The difference between the maximum of
the contrast ratios corresponding to the reference and processed images is used as a quality predictor. Human
quality scores and our proposed measure are compared with the Pearson correlation coefficient. Our
experimental results show that our method is able to accurately predict changes of perceived quality due to
contrast decrements (Pearson correlations higher than 90\%). Additionally, this method can detect changes in
contrast level in interventional x-ray images acquired with varying dose [3]. For instance, the resulting contrast
maps demonstrate reduced contrast ratios for vessel edges on X-ray images acquired at lower dose settings, i.e.,
lower distinguishability from the background, compared to higher dose acquisitions.
Conclusions
We propose a measure to compute contrast ratio by using Weber{\textquoteright}s law in local image patches. While the
proposed contrast ratio is computationally simple, this approximation of local content has shown to be useful in
measuring quality differences due to contrast decrements in images. Especially, changes in structures of interest
due to low contrast ratio can be detected by using the contrast map making our method potentially useful in Xray
imaging dose control.
References
[1] Ponomarenko N. et al., {\textquotedblleft}A New Color Image Database TID2013: Innovations and Results,{\textquotedblright} Proceedings of
ACIVS, 402-413 (2013).
[2] Larson E. and Chandler D., {\textacutedbl}Most apparent distortion: full-reference image quality assessment and the role of
strategy,{\textacutedbl} Journal of Electronic Imaging, 19 (1), 2010.
[3] Kumcu, A. et al., {\textquotedblleft}Interventional x-ray image quality measure based on a psychovisual detectability model,{\textquotedblright}
MIPS XVI, Ghent, Belgium, 2015.},
  author       = {Ortiz Jaramillo, Benhur and Kumcu, Asli and Platisa, Ljiljana and Philips, Wilfried},
  booktitle    = {Proceedings of The XVI Medical Image Perception Conference},
  keyword      = {Weber's law,Contrast ratio,local content information,image quality},
  language     = {eng},
  location     = {Ghent, Belgium},
  pages        = {34--34},
  publisher    = {MIPS conference website},
  title        = {Computing contrast ratio in medical images using local content information},
  year         = {2015},
}

Chicago
Ortiz Jaramillo, Benhur, Asli Kumcu, Ljiljana Platisa, and Wilfried Philips. 2015. “Computing Contrast Ratio in Medical Images Using Local Content Information.” In Proceedings of The XVI Medical Image Perception Conference, 34–34. http://mips.ws/: MIPS conference website.
APA
Ortiz Jaramillo, B., Kumcu, A., Platisa, L., & Philips, W. (2015). Computing contrast ratio in medical images using local content information. Proceedings of The XVI Medical Image Perception Conference (pp. 34–34). Presented at the The Medical Image Perception Conference, http://mips.ws/: MIPS conference website.
Vancouver
1.
Ortiz Jaramillo B, Kumcu A, Platisa L, Philips W. Computing contrast ratio in medical images using local content information. Proceedings of The XVI Medical Image Perception Conference. http://mips.ws/: MIPS conference website; 2015. p. 34–34.
MLA
Ortiz Jaramillo, Benhur, Asli Kumcu, Ljiljana Platisa, et al. “Computing Contrast Ratio in Medical Images Using Local Content Information.” Proceedings of The XVI Medical Image Perception Conference. http://mips.ws/: MIPS conference website, 2015. 34–34. Print.