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Tsikata E, Laíns I, Gil J, et al. Automated Brightness and Contrast Adjustment of Color Fundus Photographs for the Grading of Age-Related Macular Degeneration. Transl Vis Sci Technol. 2017;6(2):3doi: 10.1167/tvst.6.2.3.
Tsikata, E., Laíns, I., Gil, J., Marques, M., Brown, K., Mesquita, T., Melo, P., da Luz Cachulo, M., Kim, I. K., Vavvas, D., Murta, J. N., Miller, J. B., Silva, R., Miller, J. W., Chen, T. C., & Husain, D. (2017). Automated Brightness and Contrast Adjustment of Color Fundus Photographs for the Grading of Age-Related Macular Degeneration. Translational vision science & technology, 6(2), 3. https://doi.org/10.1167/tvst.6.2.3
Tsikata, Edem, et al. "Automated Brightness and Contrast Adjustment of Color Fundus Photographs for the Grading of Age-Related Macular Degeneration." Translational vision science & technology vol. 6,2 (2017): 3. doi: https://doi.org/10.1167/tvst.6.2.3
Tsikata E, Laíns I, Gil J, Marques M, Brown K, Mesquita T, Melo P, da Luz Cachulo M, Kim IK, Vavvas D, Murta JN, Miller JB, Silva R, Miller JW, Chen TC, Husain D. Automated Brightness and Contrast Adjustment of Color Fundus Photographs for the Grading of Age-Related Macular Degeneration. Transl Vis Sci Technol. 2017 Mar 13;6(2):3. doi: 10.1167/tvst.6.2.3. eCollection 2017 Mar. PMID: 28316876; PMCID: PMC5354475.
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Transl Vis Sci Technol. 2017 Mar 13;6(2):3. doi: 10.1167/tvst.6.2.3. eCollection 2017 Mar.

Automated Brightness and Contrast Adjustment of Color Fundus Photographs for the Grading of Age-Related Macular Degeneration.

Translational vision science & technology

Edem Tsikata, Inês Laíns, João Gil, Marco Marques, Kelsey Brown, Tânia Mesquita, Pedro Melo, Maria da Luz Cachulo, Ivana K Kim, Demetrios Vavvas, Joaquim N Murta, John B Miller, Rufino Silva, Joan W Miller, Teresa C Chen, Deeba Husain

Affiliations

  1. Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA ; Glaucoma Service of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
  2. Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA ; University of Coimbra, Faculty of Medicine, University of Coimbra, Coimbra, Portugal ; Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal ; Association for Biomedical Research and Innovation on Light and Image, Coimbra, Portugal ; Retina Service of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
  3. University of Coimbra, Faculty of Medicine, University of Coimbra, Coimbra, Portugal ; Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal ; Association for Biomedical Research and Innovation on Light and Image, Coimbra, Portugal.
  4. University of Coimbra, Faculty of Medicine, University of Coimbra, Coimbra, Portugal ; Association for Biomedical Research and Innovation on Light and Image, Coimbra, Portugal.
  5. Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
  6. Association for Biomedical Research and Innovation on Light and Image, Coimbra, Portugal.
  7. Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA ; Retina Service of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
  8. University of Coimbra, Faculty of Medicine, University of Coimbra, Coimbra, Portugal ; Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.

PMID: 28316876 PMCID: PMC5354475 DOI: 10.1167/tvst.6.2.3

Abstract

PURPOSE: The purpose of this study was to develop an algorithm to automatically standardize the brightness, contrast, and color balance of digital color fundus photographs used to grade AMD and to validate this algorithm by determining the effects of the standardization on image quality and disease grading.

METHODS: Seven-field color photographs of patients (>50 years) with any stage of AMD and a control group were acquired at two study sites, with either the Topcon TRC-50DX or Zeiss FF-450 Plus cameras. Field 2 photographs were analyzed. Pixel brightness values in the red, green, and blue (RGB) color channels were adjusted in custom-built software to make the mean brightness and contrast of the images equal to optimal values determined by the Age-Related Eye Disease Study (AREDS) 2 group.

RESULTS: Color photographs of 370 eyes were analyzed. We found a wide range of brightness and contrast values in the images at baseline, even for those taken with the same camera. After processing, image brightness variability (brightest image-dimmest image in a color channel) was reduced 69-fold, 62-fold, and 96-fold for the RGB channels. Contrast variability was reduced 6-fold, 8-fold, and 13-fold, respectively, after adjustment. Of the 23% images considered nongradable before adjustment, only 5.7% remained nongradable.

CONCLUSIONS: This automated software enables rapid and accurate standardization of color photographs for AMD grading.

TRANSLATIONAL RELEVANCE: This work offers the potential to be the future of assessing and grading AMD from photos for clinical research and teleimaging.

Keywords: age-related macular degeneration; automated optimization; image analysis

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