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Automated quality assessment of retinal fundus photos

  • Review Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Objective

Automated, objective and fast measurement of the image quality of single retinal fundus photos to allow a stable and reliable medical evaluation.

Methods

The proposed technique maps diagnosis-relevant criteria inspired by diagnosis procedures based on the advise of an eye expert to quantitative and objective features related to image quality. Independent from segmentation methods it combines global clustering with local sharpness and texture features for classification.

Results

On a test dataset of 301 retinal fundus images we evaluated our method on a given gold standard by human observers and compared it to a state of the art approach. An area under the ROC curve of 95.3% compared to 87.2% outperformed the state of the art approach. A significant p-value of 0.019 emphasizes the statistical difference of both approaches.

Conclusions

The combination of local and global image statistics models the defined quality criteria and automatically produces reliable and objective results in determining the image quality of retinal fundus photos.

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References

  1. Abràmoff MD, Niemeijer M, Suttorp-Schulten MS, Viergever MA, Russell SR, van Ginneken B (2008) Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care 31(2): 193–198

    Article  PubMed  Google Scholar 

  2. Bock R, Meier J, Nyúl LG, Hornegger J, Michelson G (2010) Glaucoma risk index: automated glaucoma detection from color fundus images. Med Image Anal 14(3): 471–481

    Article  PubMed  Google Scholar 

  3. Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S, Usher D (2002) Automated detection of diabetic retinopathy on digital fundus images. Diabetic Med 19(2): 105–112

    Article  CAS  PubMed  Google Scholar 

  4. Abràmoff MD, Suttorp-Schulten M (2005) screening for diabetic retinopathy in a primary care population: the eye check project. Telemed e-Health 11(6): 668–674

    Article  Google Scholar 

  5. Eskicioglu AM, Fisher PS (1995) Quality measures and their performance. IEEE Trans Commun 3(12): 2959–2965

    Article  Google Scholar 

  6. Avcıbaş İ, Sankur B, Sayood K (2002) Statistical evaluation of image quality measures. J Electron Imaging 11(2): 206–223

    Article  Google Scholar 

  7. Wang Z, Bovik AC, Lu L (2002) Why is image quality assessment so difficult?. IEEE Int Conf Acoust Speech Signal Process Proc 4: 3313–3316

    Google Scholar 

  8. Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF (2006) Automated assessment of diabetic retinal image quality based on clarity and field definition. Investig Ophthalmol Visual Sci 47(3): 1120–1125

    Article  Google Scholar 

  9. Giancardo L, Abràmoff MD, Chaum E, Karnowski TP, Meriaudeau F, Tobin KW Jr (2008) Elliptical local vessel density: a fast and robust quality metric for retinal images, Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th annual international conference of the IEEE, pp 3534–3537

  10. Lalonde M, Gagnony L, Boucher M-C (2001)Automatic visual quality assessment in optical fundus images. Proceedings of Vision Interface (VI 2001), pp 259–264

  11. Lee SC, Wang Y (1999) Automatic retinal image quality assessment and enhancement. Proc SPIE 3661: 1581–1590

    Article  Google Scholar 

  12. Niemeijer M, Abràmoff MD, Ginneken B (2006) Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med Image Anal 10(6): 888–898

    Article  PubMed  Google Scholar 

  13. van Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6): 610–621

    Article  Google Scholar 

  14. Chang C-C, Lin C-J (2001) “LIBSVM”: a library for support vector machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm

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Authors and Affiliations

  1. Pattern Recognition Lab, Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058, Erlangen, Germany

    Jan Paulus, Jörg Meier, Rüdiger Bock & Joachim Hornegger

  2. Department of Ophthalmology, Graduate School in Advanced Optical Technologies (SAOT), Interdisciplinary Center of Ophthalmic Preventive Medicine and Imaging (IZPI), Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, 91054, Erlangen, Germany

    Georg Michelson

Authors
  1. Jan Paulus
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  2. Jörg Meier
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  3. Rüdiger Bock
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  4. Joachim Hornegger
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  5. Georg Michelson
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Corresponding author

Correspondence to Jan Paulus.

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Paulus, J., Meier, J., Bock, R. et al. Automated quality assessment of retinal fundus photos. Int J CARS 5, 557–564 (2010). https://doi.org/10.1007/s11548-010-0479-7

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  • DOI: https://doi.org/10.1007/s11548-010-0479-7

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