Abstract

Rapid development in the field of ophthalmology has increased the demand of computer aided diagnosis of various eye diseases. Papilledema is an eye disease in which the optic disc of the eye is swelled due to an increase in intracranial pressure. This increased pressure can cause severe encephalic complications like abscess, tumors, meningitis or encephalitis, which may lead to a patient’s death. Although there have been several papilledema case studies reported from a medical point of view, only a few researchers have presented automated algorithms for this problem. This paper presents a novel computer aided system which aims to automatically detect papilledema from fundus images. Firstly, the fundus images are preprocessed by going through optic disc detection and vessel segmentation. After preprocessing, a total of 26 different features are extracted to capture possible changes in the optic disc due to papilledema. These features are further divided into four categories based upon their color, textural, vascular and disc margin obscuration properties. The best features are then selected and combined to form a feature matrix that is used to distinguish between normal images and images with papilledema using the supervised support vector machine (SVM) classifier. The proposed method is tested on 160 fundus images obtained from two different data sets i.e. structured analysis of retina (STARE), which is a publicly available data set, and our local data set that has been acquired from the Armed Forces Institute of Ophthalmology (AFIO). The STARE data set contained 90 and our local data set contained 70 fundus images respectively. These annotations have been performed with the help of two ophthalmologists. We report detection accuracies of 95.6% for STARE, 87.4% for the local data set, and 85.9% for the combined STARE and local data sets. The proposed system is fast and robust in detecting papilledema from fundus images with promising results. This will aid physicians in clinical assessment of fundus images. It will not take away the role of physicians, but will rather help them in the time consuming process of screening fundus images.

© 2017 Optical Society of America

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References

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  1. J. R, Basic Ophthalmology, 4th ed., (New Delhi, 2009).
  2. H. S. Nguyen, K. M. Haider, and L. L. Ackerman, “Unusual causes of papilledema: Two illustrative cases,” Surg. Neurol. Int. 4(60), 60 (2013).
    [PubMed]
  3. C. L. Fraser, M. A. Ridha, V. Biousse, and N. J. Newman, “Vitreous Hemorrhage Secondary to Optociliary Shunt Vessels from Papilledema,” J. Neuroophthalmol. 32(4), 332–334 (2012).
    [Crossref] [PubMed]
  4. L. Tang, R. H. Kardon, J.-K. Wang, M. K. Garvin, K. Lee, and M. D. Abràmoff, “Quantitative Evaluation of Papilledema from Stereoscopic Color Fundus Photographs,” Invest. Ophthalmol. Vis. Sci. 53(8), 4490–4497 (2012).
    [Crossref] [PubMed]
  5. E. Z. Karam and T. R. Hedges, “Optical coherence tomography of the retinal nerve fibre layer in mild papilloedema and pseudopapilloedema,” Br. J. Ophthalmol. 89, 294–298 (2005).
  6. S. Echegaray, G. Zamora, H. Yu, W. Luo, P. Soliz, and R. Kardon, “Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema,” Invest. Ophthalmol. Vis. Sci. 52(10), 7470–7478 (2011).
    [Crossref] [PubMed]
  7. K. N. Fatima, M. U. Akram, and S. A. Bazaz, “Papilledema Detection in Fundus Images Using Hybrid Feature Set,” inProceedings of IEEE IT Convergence and Security (ICITCS), Kuala Lampur, 2015.
    [Crossref]
  8. K. Yousaf, M. U. Akram, U. Ali, and S. A. Sheikh, “Assessment of papilledema using fundus images”, IEEE International Conference and Imaging Systems and Techniques (IST), October 2016.
    [Crossref]
  9. A. Usman, S. A. Khitran, and Y. Nadeem, “A Robust Algorithm for Optic Disc Segmentation from Colored Fundus Images,” in Proceedings of International Conference on Image Analysis and Recognition (ICIAR), Vilamoura, Portugal, 2014.
    [Crossref]
  10. M. U. Akram and S. A. Khan, “Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy,” Eng. Comput. 29(2), 165–173 (2013).
    [Crossref]
  11. A. A. Salam, T. Khalil, M. U. Akram, A. Jameel, and I. Basit, “Automated detection of glaucoma using structural and non structural features,” Springerplus 5(1), 1519 (2016).
    [Crossref] [PubMed]
  12. L. A. Ruiz, A. Fdez-sarría, and J. A. Recio, “Texture feature extraction for classification of remote sensing data using wavelet decomposition: a comparative study,” International Archives of Photogrammetry and Remote Sensing (ISPRS), vol. 35, 2004.
  13. J. Lei, F. L. Wang, H. Deng, and D. Miao, “Artificial Intelligence and Computational Intelligence,” in AICI, Chengdu, China, 2012.
  14. A. Gebejes and R. Huertas, “Texture Characterization based on Grey-Level Co-occurrence Matrix,” in Proceedings of the Conference of Informatics and Management Sciences, pp.375–378, 2013.
  15. A. L. Ion, “Methods for knowledge discovery in images,” Inf. Technol. Control 38(1), 43–50 (2009).
  16. L. Frisén, “Swelling of the optic nerve head: a staging scheme,” J. Neurol. Neurosurg. Psychiatry 45(1), 13–18 (1982).
    [Crossref] [PubMed]
  17. OD swelling annotations in STARE, http://cecas.clemson.edu/~ahoover/stare/manifestations/man19.htm
  18. C. J. Scott, R. H. Kardon, A. G. Lee, L. Frisén, and M. Wall, “Diagnosis and Grading of Papilledema in Patients With Raised Intracranial Pressure Using Optical Coherence tomography vs Clinical Expert Assessment Using a Clinical Staging Scale,” Arch. Ophthalmol. 128(6), 705–711 (2010).
    [Crossref] [PubMed]

2016 (1)

A. A. Salam, T. Khalil, M. U. Akram, A. Jameel, and I. Basit, “Automated detection of glaucoma using structural and non structural features,” Springerplus 5(1), 1519 (2016).
[Crossref] [PubMed]

2013 (2)

M. U. Akram and S. A. Khan, “Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy,” Eng. Comput. 29(2), 165–173 (2013).
[Crossref]

H. S. Nguyen, K. M. Haider, and L. L. Ackerman, “Unusual causes of papilledema: Two illustrative cases,” Surg. Neurol. Int. 4(60), 60 (2013).
[PubMed]

2012 (2)

C. L. Fraser, M. A. Ridha, V. Biousse, and N. J. Newman, “Vitreous Hemorrhage Secondary to Optociliary Shunt Vessels from Papilledema,” J. Neuroophthalmol. 32(4), 332–334 (2012).
[Crossref] [PubMed]

L. Tang, R. H. Kardon, J.-K. Wang, M. K. Garvin, K. Lee, and M. D. Abràmoff, “Quantitative Evaluation of Papilledema from Stereoscopic Color Fundus Photographs,” Invest. Ophthalmol. Vis. Sci. 53(8), 4490–4497 (2012).
[Crossref] [PubMed]

2011 (1)

S. Echegaray, G. Zamora, H. Yu, W. Luo, P. Soliz, and R. Kardon, “Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema,” Invest. Ophthalmol. Vis. Sci. 52(10), 7470–7478 (2011).
[Crossref] [PubMed]

2010 (1)

C. J. Scott, R. H. Kardon, A. G. Lee, L. Frisén, and M. Wall, “Diagnosis and Grading of Papilledema in Patients With Raised Intracranial Pressure Using Optical Coherence tomography vs Clinical Expert Assessment Using a Clinical Staging Scale,” Arch. Ophthalmol. 128(6), 705–711 (2010).
[Crossref] [PubMed]

2009 (1)

A. L. Ion, “Methods for knowledge discovery in images,” Inf. Technol. Control 38(1), 43–50 (2009).

2005 (1)

E. Z. Karam and T. R. Hedges, “Optical coherence tomography of the retinal nerve fibre layer in mild papilloedema and pseudopapilloedema,” Br. J. Ophthalmol. 89, 294–298 (2005).

1982 (1)

L. Frisén, “Swelling of the optic nerve head: a staging scheme,” J. Neurol. Neurosurg. Psychiatry 45(1), 13–18 (1982).
[Crossref] [PubMed]

Abràmoff, M. D.

L. Tang, R. H. Kardon, J.-K. Wang, M. K. Garvin, K. Lee, and M. D. Abràmoff, “Quantitative Evaluation of Papilledema from Stereoscopic Color Fundus Photographs,” Invest. Ophthalmol. Vis. Sci. 53(8), 4490–4497 (2012).
[Crossref] [PubMed]

Ackerman, L. L.

H. S. Nguyen, K. M. Haider, and L. L. Ackerman, “Unusual causes of papilledema: Two illustrative cases,” Surg. Neurol. Int. 4(60), 60 (2013).
[PubMed]

Akram, M. U.

A. A. Salam, T. Khalil, M. U. Akram, A. Jameel, and I. Basit, “Automated detection of glaucoma using structural and non structural features,” Springerplus 5(1), 1519 (2016).
[Crossref] [PubMed]

M. U. Akram and S. A. Khan, “Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy,” Eng. Comput. 29(2), 165–173 (2013).
[Crossref]

K. N. Fatima, M. U. Akram, and S. A. Bazaz, “Papilledema Detection in Fundus Images Using Hybrid Feature Set,” inProceedings of IEEE IT Convergence and Security (ICITCS), Kuala Lampur, 2015.
[Crossref]

Basit, I.

A. A. Salam, T. Khalil, M. U. Akram, A. Jameel, and I. Basit, “Automated detection of glaucoma using structural and non structural features,” Springerplus 5(1), 1519 (2016).
[Crossref] [PubMed]

Bazaz, S. A.

K. N. Fatima, M. U. Akram, and S. A. Bazaz, “Papilledema Detection in Fundus Images Using Hybrid Feature Set,” inProceedings of IEEE IT Convergence and Security (ICITCS), Kuala Lampur, 2015.
[Crossref]

Biousse, V.

C. L. Fraser, M. A. Ridha, V. Biousse, and N. J. Newman, “Vitreous Hemorrhage Secondary to Optociliary Shunt Vessels from Papilledema,” J. Neuroophthalmol. 32(4), 332–334 (2012).
[Crossref] [PubMed]

Echegaray, S.

S. Echegaray, G. Zamora, H. Yu, W. Luo, P. Soliz, and R. Kardon, “Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema,” Invest. Ophthalmol. Vis. Sci. 52(10), 7470–7478 (2011).
[Crossref] [PubMed]

Fatima, K. N.

K. N. Fatima, M. U. Akram, and S. A. Bazaz, “Papilledema Detection in Fundus Images Using Hybrid Feature Set,” inProceedings of IEEE IT Convergence and Security (ICITCS), Kuala Lampur, 2015.
[Crossref]

Fraser, C. L.

C. L. Fraser, M. A. Ridha, V. Biousse, and N. J. Newman, “Vitreous Hemorrhage Secondary to Optociliary Shunt Vessels from Papilledema,” J. Neuroophthalmol. 32(4), 332–334 (2012).
[Crossref] [PubMed]

Frisén, L.

C. J. Scott, R. H. Kardon, A. G. Lee, L. Frisén, and M. Wall, “Diagnosis and Grading of Papilledema in Patients With Raised Intracranial Pressure Using Optical Coherence tomography vs Clinical Expert Assessment Using a Clinical Staging Scale,” Arch. Ophthalmol. 128(6), 705–711 (2010).
[Crossref] [PubMed]

L. Frisén, “Swelling of the optic nerve head: a staging scheme,” J. Neurol. Neurosurg. Psychiatry 45(1), 13–18 (1982).
[Crossref] [PubMed]

Garvin, M. K.

L. Tang, R. H. Kardon, J.-K. Wang, M. K. Garvin, K. Lee, and M. D. Abràmoff, “Quantitative Evaluation of Papilledema from Stereoscopic Color Fundus Photographs,” Invest. Ophthalmol. Vis. Sci. 53(8), 4490–4497 (2012).
[Crossref] [PubMed]

Gebejes, A.

A. Gebejes and R. Huertas, “Texture Characterization based on Grey-Level Co-occurrence Matrix,” in Proceedings of the Conference of Informatics and Management Sciences, pp.375–378, 2013.

Haider, K. M.

H. S. Nguyen, K. M. Haider, and L. L. Ackerman, “Unusual causes of papilledema: Two illustrative cases,” Surg. Neurol. Int. 4(60), 60 (2013).
[PubMed]

Hedges, T. R.

E. Z. Karam and T. R. Hedges, “Optical coherence tomography of the retinal nerve fibre layer in mild papilloedema and pseudopapilloedema,” Br. J. Ophthalmol. 89, 294–298 (2005).

Huertas, R.

A. Gebejes and R. Huertas, “Texture Characterization based on Grey-Level Co-occurrence Matrix,” in Proceedings of the Conference of Informatics and Management Sciences, pp.375–378, 2013.

Ion, A. L.

A. L. Ion, “Methods for knowledge discovery in images,” Inf. Technol. Control 38(1), 43–50 (2009).

Jameel, A.

A. A. Salam, T. Khalil, M. U. Akram, A. Jameel, and I. Basit, “Automated detection of glaucoma using structural and non structural features,” Springerplus 5(1), 1519 (2016).
[Crossref] [PubMed]

Karam, E. Z.

E. Z. Karam and T. R. Hedges, “Optical coherence tomography of the retinal nerve fibre layer in mild papilloedema and pseudopapilloedema,” Br. J. Ophthalmol. 89, 294–298 (2005).

Kardon, R.

S. Echegaray, G. Zamora, H. Yu, W. Luo, P. Soliz, and R. Kardon, “Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema,” Invest. Ophthalmol. Vis. Sci. 52(10), 7470–7478 (2011).
[Crossref] [PubMed]

Kardon, R. H.

L. Tang, R. H. Kardon, J.-K. Wang, M. K. Garvin, K. Lee, and M. D. Abràmoff, “Quantitative Evaluation of Papilledema from Stereoscopic Color Fundus Photographs,” Invest. Ophthalmol. Vis. Sci. 53(8), 4490–4497 (2012).
[Crossref] [PubMed]

C. J. Scott, R. H. Kardon, A. G. Lee, L. Frisén, and M. Wall, “Diagnosis and Grading of Papilledema in Patients With Raised Intracranial Pressure Using Optical Coherence tomography vs Clinical Expert Assessment Using a Clinical Staging Scale,” Arch. Ophthalmol. 128(6), 705–711 (2010).
[Crossref] [PubMed]

Khalil, T.

A. A. Salam, T. Khalil, M. U. Akram, A. Jameel, and I. Basit, “Automated detection of glaucoma using structural and non structural features,” Springerplus 5(1), 1519 (2016).
[Crossref] [PubMed]

Khan, S. A.

M. U. Akram and S. A. Khan, “Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy,” Eng. Comput. 29(2), 165–173 (2013).
[Crossref]

Khitran, S. A.

A. Usman, S. A. Khitran, and Y. Nadeem, “A Robust Algorithm for Optic Disc Segmentation from Colored Fundus Images,” in Proceedings of International Conference on Image Analysis and Recognition (ICIAR), Vilamoura, Portugal, 2014.
[Crossref]

Lee, A. G.

C. J. Scott, R. H. Kardon, A. G. Lee, L. Frisén, and M. Wall, “Diagnosis and Grading of Papilledema in Patients With Raised Intracranial Pressure Using Optical Coherence tomography vs Clinical Expert Assessment Using a Clinical Staging Scale,” Arch. Ophthalmol. 128(6), 705–711 (2010).
[Crossref] [PubMed]

Lee, K.

L. Tang, R. H. Kardon, J.-K. Wang, M. K. Garvin, K. Lee, and M. D. Abràmoff, “Quantitative Evaluation of Papilledema from Stereoscopic Color Fundus Photographs,” Invest. Ophthalmol. Vis. Sci. 53(8), 4490–4497 (2012).
[Crossref] [PubMed]

Luo, W.

S. Echegaray, G. Zamora, H. Yu, W. Luo, P. Soliz, and R. Kardon, “Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema,” Invest. Ophthalmol. Vis. Sci. 52(10), 7470–7478 (2011).
[Crossref] [PubMed]

Nadeem, Y.

A. Usman, S. A. Khitran, and Y. Nadeem, “A Robust Algorithm for Optic Disc Segmentation from Colored Fundus Images,” in Proceedings of International Conference on Image Analysis and Recognition (ICIAR), Vilamoura, Portugal, 2014.
[Crossref]

Newman, N. J.

C. L. Fraser, M. A. Ridha, V. Biousse, and N. J. Newman, “Vitreous Hemorrhage Secondary to Optociliary Shunt Vessels from Papilledema,” J. Neuroophthalmol. 32(4), 332–334 (2012).
[Crossref] [PubMed]

Nguyen, H. S.

H. S. Nguyen, K. M. Haider, and L. L. Ackerman, “Unusual causes of papilledema: Two illustrative cases,” Surg. Neurol. Int. 4(60), 60 (2013).
[PubMed]

Ridha, M. A.

C. L. Fraser, M. A. Ridha, V. Biousse, and N. J. Newman, “Vitreous Hemorrhage Secondary to Optociliary Shunt Vessels from Papilledema,” J. Neuroophthalmol. 32(4), 332–334 (2012).
[Crossref] [PubMed]

Salam, A. A.

A. A. Salam, T. Khalil, M. U. Akram, A. Jameel, and I. Basit, “Automated detection of glaucoma using structural and non structural features,” Springerplus 5(1), 1519 (2016).
[Crossref] [PubMed]

Scott, C. J.

C. J. Scott, R. H. Kardon, A. G. Lee, L. Frisén, and M. Wall, “Diagnosis and Grading of Papilledema in Patients With Raised Intracranial Pressure Using Optical Coherence tomography vs Clinical Expert Assessment Using a Clinical Staging Scale,” Arch. Ophthalmol. 128(6), 705–711 (2010).
[Crossref] [PubMed]

Soliz, P.

S. Echegaray, G. Zamora, H. Yu, W. Luo, P. Soliz, and R. Kardon, “Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema,” Invest. Ophthalmol. Vis. Sci. 52(10), 7470–7478 (2011).
[Crossref] [PubMed]

Tang, L.

L. Tang, R. H. Kardon, J.-K. Wang, M. K. Garvin, K. Lee, and M. D. Abràmoff, “Quantitative Evaluation of Papilledema from Stereoscopic Color Fundus Photographs,” Invest. Ophthalmol. Vis. Sci. 53(8), 4490–4497 (2012).
[Crossref] [PubMed]

Usman, A.

A. Usman, S. A. Khitran, and Y. Nadeem, “A Robust Algorithm for Optic Disc Segmentation from Colored Fundus Images,” in Proceedings of International Conference on Image Analysis and Recognition (ICIAR), Vilamoura, Portugal, 2014.
[Crossref]

Wall, M.

C. J. Scott, R. H. Kardon, A. G. Lee, L. Frisén, and M. Wall, “Diagnosis and Grading of Papilledema in Patients With Raised Intracranial Pressure Using Optical Coherence tomography vs Clinical Expert Assessment Using a Clinical Staging Scale,” Arch. Ophthalmol. 128(6), 705–711 (2010).
[Crossref] [PubMed]

Wang, J.-K.

L. Tang, R. H. Kardon, J.-K. Wang, M. K. Garvin, K. Lee, and M. D. Abràmoff, “Quantitative Evaluation of Papilledema from Stereoscopic Color Fundus Photographs,” Invest. Ophthalmol. Vis. Sci. 53(8), 4490–4497 (2012).
[Crossref] [PubMed]

Yu, H.

S. Echegaray, G. Zamora, H. Yu, W. Luo, P. Soliz, and R. Kardon, “Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema,” Invest. Ophthalmol. Vis. Sci. 52(10), 7470–7478 (2011).
[Crossref] [PubMed]

Zamora, G.

S. Echegaray, G. Zamora, H. Yu, W. Luo, P. Soliz, and R. Kardon, “Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema,” Invest. Ophthalmol. Vis. Sci. 52(10), 7470–7478 (2011).
[Crossref] [PubMed]

Arch. Ophthalmol. (1)

C. J. Scott, R. H. Kardon, A. G. Lee, L. Frisén, and M. Wall, “Diagnosis and Grading of Papilledema in Patients With Raised Intracranial Pressure Using Optical Coherence tomography vs Clinical Expert Assessment Using a Clinical Staging Scale,” Arch. Ophthalmol. 128(6), 705–711 (2010).
[Crossref] [PubMed]

Br. J. Ophthalmol. (1)

E. Z. Karam and T. R. Hedges, “Optical coherence tomography of the retinal nerve fibre layer in mild papilloedema and pseudopapilloedema,” Br. J. Ophthalmol. 89, 294–298 (2005).

Eng. Comput. (1)

M. U. Akram and S. A. Khan, “Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy,” Eng. Comput. 29(2), 165–173 (2013).
[Crossref]

Inf. Technol. Control (1)

A. L. Ion, “Methods for knowledge discovery in images,” Inf. Technol. Control 38(1), 43–50 (2009).

Invest. Ophthalmol. Vis. Sci. (2)

S. Echegaray, G. Zamora, H. Yu, W. Luo, P. Soliz, and R. Kardon, “Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema,” Invest. Ophthalmol. Vis. Sci. 52(10), 7470–7478 (2011).
[Crossref] [PubMed]

L. Tang, R. H. Kardon, J.-K. Wang, M. K. Garvin, K. Lee, and M. D. Abràmoff, “Quantitative Evaluation of Papilledema from Stereoscopic Color Fundus Photographs,” Invest. Ophthalmol. Vis. Sci. 53(8), 4490–4497 (2012).
[Crossref] [PubMed]

J. Neurol. Neurosurg. Psychiatry (1)

L. Frisén, “Swelling of the optic nerve head: a staging scheme,” J. Neurol. Neurosurg. Psychiatry 45(1), 13–18 (1982).
[Crossref] [PubMed]

J. Neuroophthalmol. (1)

C. L. Fraser, M. A. Ridha, V. Biousse, and N. J. Newman, “Vitreous Hemorrhage Secondary to Optociliary Shunt Vessels from Papilledema,” J. Neuroophthalmol. 32(4), 332–334 (2012).
[Crossref] [PubMed]

Springerplus (1)

A. A. Salam, T. Khalil, M. U. Akram, A. Jameel, and I. Basit, “Automated detection of glaucoma using structural and non structural features,” Springerplus 5(1), 1519 (2016).
[Crossref] [PubMed]

Surg. Neurol. Int. (1)

H. S. Nguyen, K. M. Haider, and L. L. Ackerman, “Unusual causes of papilledema: Two illustrative cases,” Surg. Neurol. Int. 4(60), 60 (2013).
[PubMed]

Other (8)

J. R, Basic Ophthalmology, 4th ed., (New Delhi, 2009).

K. N. Fatima, M. U. Akram, and S. A. Bazaz, “Papilledema Detection in Fundus Images Using Hybrid Feature Set,” inProceedings of IEEE IT Convergence and Security (ICITCS), Kuala Lampur, 2015.
[Crossref]

K. Yousaf, M. U. Akram, U. Ali, and S. A. Sheikh, “Assessment of papilledema using fundus images”, IEEE International Conference and Imaging Systems and Techniques (IST), October 2016.
[Crossref]

A. Usman, S. A. Khitran, and Y. Nadeem, “A Robust Algorithm for Optic Disc Segmentation from Colored Fundus Images,” in Proceedings of International Conference on Image Analysis and Recognition (ICIAR), Vilamoura, Portugal, 2014.
[Crossref]

L. A. Ruiz, A. Fdez-sarría, and J. A. Recio, “Texture feature extraction for classification of remote sensing data using wavelet decomposition: a comparative study,” International Archives of Photogrammetry and Remote Sensing (ISPRS), vol. 35, 2004.

J. Lei, F. L. Wang, H. Deng, and D. Miao, “Artificial Intelligence and Computational Intelligence,” in AICI, Chengdu, China, 2012.

A. Gebejes and R. Huertas, “Texture Characterization based on Grey-Level Co-occurrence Matrix,” in Proceedings of the Conference of Informatics and Management Sciences, pp.375–378, 2013.

OD swelling annotations in STARE, http://cecas.clemson.edu/~ahoover/stare/manifestations/man19.htm

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Figures (11)

Fig. 1
Fig. 1 Fundus image with papilledema: It can be noted that the optic disc boundary is blurred and blood vessels are dilated.
Fig. 2
Fig. 2 Block diagram of proposed automatic papilledema detection system.
Fig. 3
Fig. 3 Fundus image with papilledema.
Fig. 4
Fig. 4 The ROIs obtained because of preprocessing step (a) original fundus image (b) ROI after optic disc localization (c) ROI after vessel segmentation.
Fig. 5
Fig. 5 Selected pixels and their respective values on disc boundary of (a) normal image, and (b) image with papilledema.
Fig. 6
Fig. 6 Automated extraction of 8 selected pixels, (a) radial lines are shown in white color which are used to extract the optic disc boundary pixels at 8 equal intervals, (b) extracted pixels are mapped onto the optic disc image.
Fig. 7
Fig. 7 The selected profile paths in case of (a) healthy fundus image, and (b) image with papilledema, and their computed profiles shown respectively in (c) and (d).
Fig. 8
Fig. 8 Fundus images, their vessel segmentation and respective VDI.
Fig. 9
Fig. 9 The division of OD into poles and subdivision of nasal pole.
Fig. 10
Fig. 10 Effects of number of top features on percentage accuracy, sensitivity, and specificity results.
Fig. 11
Fig. 11 Poor quality fundus scans from our local AFIO data set.

Tables (7)

Tables Icon

Table 1 Modified Friesen Scale [18]

Tables Icon

Table 2 Summary of data set used in this research

Tables Icon

Table 3 Summary of extracted features

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Table 4 Performance of features calculated using Wilcoxon rank sum test

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Table 5 Group-wise features and their evaluation results

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Table 6 Proposed system performance

Tables Icon

Table 7 Performance comparison

Metrics