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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2014 - 25
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An Automatic Optic Disk Detection and Segmentation System using Multi-level Thresholding

KARASULU, B. See more information about KARASULU, B. on SCOPUS See more information about KARASULU, B. on IEEExplore See more information about KARASULU, B. on Web of Science
 
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Download PDF pdficon (1,105 KB) | Citation | Downloads: 452 | Views: 1,797

Author keywords
image processing, image segmentation, biomedical imaging, digital imaging, retinal image database

References keywords
optic(17), disc(13), detection(12), images(10), image(9), retinal(8), fundus(8), automatic(8), segmentation(6), methods(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-05-31
Volume 14, Issue 2, Year 2014, On page(s): 161 - 172
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.02025
Web of Science Accession Number: 000340868100025
SCOPUS ID: 84901818521

Abstract
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Optic disk (OD) boundary localization is a substantial problem in ophthalmic image processing research area. In order to segment the region of OD, we developed an automatic system which involves a multi-level thresholding. The OD segmentation results of the system in terms of average precision, recall and accuracy for DRIVE database are 98.88%, 99.91%, 98.83%, for STARE database are 98.62%, 97.38%, 96.11%, and for DIARETDB1 database are 99.29%, 99.90%, 99.20%, respectively. The experimental results show that our system works properly on retinal image databases with diseased retinas, diabetic signs, and a large degree of quality variability.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [SCOPUS Times Cited 28]


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[13] D.-Y. Huang, T.-W. Lin and W.-C. Hu, "Automatic Multilevel Thresholding Based On Two-Stage Otsu's Method With Cluster Determination By Valley Estimation", International Journal of Innovative Computing, Information and Control, vol. 7, no. 10, pp. 5631-5644, 2011.

[14] N. Otsu, "A Threshold Selection Method from Gray-level Histograms", IEEE Trans. on Syst. Man Cybern, vol. 9, pp. 62-66 , 1979.
[CrossRef]


[15] M. Niemeijer and B. V. Ginneken, "Digital Retinal Images for Vessel Extraction image (DRIVE) database", 2002, [Online] Available: Temporary on-line reference link removed - see the PDF document

[16] A. Hoover, "STructured Analysis of the Retina (STARE) database", 2000, [Online] Available: Temporary on-line reference link removed - see the PDF document

[17] T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lenu, I. Sorri, A. Raninen, R. Voutilainen, J. Pietilä, H. Käluiäinen and H. Uusitalo, "Diaretdb1 Diabetic Retinopathy Database and Evaluation Protocol", in Proc. the Medical Image Understanding and Analysis, Aberystwyth, UK, pp. 61-65, 2007.

[18] A. A. A. Youssif, A. Z. Ghalwash and A. A. S. A. Ghoneim, "Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter", IEEE Trans Med Imaging, vol. 27, no. 1, pp. 11-18 , 2008.
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[19] M. Niemeijer, B. V. Ginneken, F. B. terHaar and M. D. Abramoff, "Automatic detection of the optic disc, fovea and vascular arch in digital color photographs of the retina", in Proc. the British Machine Vision Conference, pp. 17.1-17.10, 2005.
[CrossRef] [SCOPUS Times Cited 8]


[20] T. Walter and J. C. Klein, "Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques". in Proc. Second International Symposium of Medical Data Anlaysis (ISMDA), pp. 282-287, 2001.

[21] R. J. Qureshi, L. Kovacs, B. Harangi, B. Nagy, T. Peto and H. Hajdu, "Combining algorithms for automatic detection of optic disc and macula in fundus images", Computer Vision and Image Understanding, vol. 116, no. 1, pp. 138-145, 2012.
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[23] D. Welfer, J. Scharcanski and D. R. Marinho, "A Morphologic two-stage approach for automated optic disk detection in color eye fundus images". Pattern Recogn Letters, vol. 34, no. 5, pp. 476-485, 2013.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 13]


[24] P.-S. Liao, T.-S. Chen and P.-C. Chung, "A fast algorithm for multilevel thresholding", Journal of Information Science and Engineering, vol. 17, no. 5, pp. 713-727, 2001.

[25] X. Zhu, R. M. Rangayyan and A. L. Ells, "Digital Image Processing for Ophthalmology: Detection of the Optic Nerve Head", Synthesis Lectures on Biomedical Engineering, Morgan & Claypool Publishers, vol. 6, no. 1, pp. 1-106, 2011.
[CrossRef] [SCOPUS Times Cited 4]


[26] M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen and S. A. Barman, "Blood vessel segmentation methodologies in retinal images - A survey", Comput Methods Programs Biomed, vol. 108, no. 1, pp. 407-433, 2012.
[CrossRef] [Web of Science Times Cited 180] [SCOPUS Times Cited 245]


[27] The GNU Image Manipulation Program website, 2014, [Online] Available: Temporary on-line reference link removed - see the PDF document

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[29] A. Baumann, M. Boltz, J. Ebling, M. Koenig, H. S. Loos, M. Merkel, W. Niem, J. K. Warzelhan and J. Yu, "A review and comparison of measures for automatic video surveillance systems", EURASIP Journal on Image and Video Processing, Article ID: 824726, pp. 1-30, 2008.
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[31] The OD D&S Program website, 2014, [Online] Available: Temporary on-line reference link removed - see the PDF document



References Weight

Web of Science® Citations for all references: 800 TCR
SCOPUS® Citations for all references: 1,219 TCR

Web of Science® Average Citations per reference: 25 ACR
SCOPUS® Average Citations per reference: 38 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-04-23 04:07 in 127 seconds.




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