Detail publikace

Automatic no-reference quality assessment for retinal fundus images using vessel segmentation

Originální název

Automatic no-reference quality assessment for retinal fundus images using vessel segmentation

Anglický název

Automatic no-reference quality assessment for retinal fundus images using vessel segmentation

Jazyk

en

Originální abstrakt

Fundus imaging is the most commonly used modality to collect information about the human eye background. Objective and quantitative assessment of quality for the acquired images is essential for manual, computer-aided and fully automatic diagnosis. In this paper, we present a noreference quality metric to quantify image noise and blur and its application to fundus image quality assessment. The proposed metric takes the vessel tree visible on the retina as guidance to determine an image quality score. In our experiments, the performance of this approach is demonstrated by correlation analysis with the established full-reference metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM). We found a Spearman rank correlation for PSNR and SSIM of 0.89 and 0.91. For real data, our metric correlates reasonable to a human observer, indicating high agreement to human visual perception.

Anglický abstrakt

Fundus imaging is the most commonly used modality to collect information about the human eye background. Objective and quantitative assessment of quality for the acquired images is essential for manual, computer-aided and fully automatic diagnosis. In this paper, we present a noreference quality metric to quantify image noise and blur and its application to fundus image quality assessment. The proposed metric takes the vessel tree visible on the retina as guidance to determine an image quality score. In our experiments, the performance of this approach is demonstrated by correlation analysis with the established full-reference metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM). We found a Spearman rank correlation for PSNR and SSIM of 0.89 and 0.91. For real data, our metric correlates reasonable to a human observer, indicating high agreement to human visual perception.

BibTex


@inproceedings{BUT99737,
  author="Thomas {Köhler} and Attila {Budai} and Martin {Kraus} and Jan {Odstrčilík} and Georg {Michelson} and Joachim {Hornegger}",
  title="Automatic no-reference quality assessment for retinal fundus images using vessel segmentation",
  annote="Fundus imaging is the most commonly used modality to collect information about the human eye background. Objective and quantitative assessment of quality for the acquired images is essential for manual, computer-aided and fully automatic diagnosis. In this paper, we present a noreference quality metric to quantify image noise and blur and its application to fundus image quality assessment. The proposed metric takes the vessel tree visible on the retina as guidance to determine an image quality score. In our experiments, the performance of this approach is demonstrated by correlation analysis with the established full-reference metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM). We found a Spearman rank correlation for PSNR and SSIM of 0.89 and 0.91. For real data, our metric correlates reasonable to a human observer, indicating high agreement to human visual perception.",
  address="University of Porto",
  booktitle="26th IEEE International Symposium on Computer-Based Medical Systems",
  chapter="99737",
  edition="26",
  howpublished="electronic, physical medium",
  institution="University of Porto",
  year="2013",
  month="june",
  pages="95--100",
  publisher="University of Porto",
  type="conference paper"
}