Click to open the HelpDesk interface
AECE - Front page banner



JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Nov 2018
Next issue: Feb 2019
Avg review time: 81 days


Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


2,166,319 unique visits
Since November 1, 2009

No robots online now


SCImago Journal & Country Rank

SEARCH ENGINES - Google Pagerank


Anycast DNS Hosting

 Volume 18 (2018)
     »   Issue 4 / 2018
     »   Issue 3 / 2018
     »   Issue 2 / 2018
     »   Issue 1 / 2018
 Volume 17 (2017)
     »   Issue 4 / 2017
     »   Issue 3 / 2017
     »   Issue 2 / 2017
     »   Issue 1 / 2017
 Volume 16 (2016)
     »   Issue 4 / 2016
     »   Issue 3 / 2016
     »   Issue 2 / 2016
     »   Issue 1 / 2016
 Volume 15 (2015)
     »   Issue 4 / 2015
     »   Issue 3 / 2015
     »   Issue 2 / 2015
     »   Issue 1 / 2015
  View all issues  


Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


  2/2014 - 25
View TOC | « Previous Article | Next Article »

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
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,105 KB) | Citation | Downloads: 507 | Views: 2,492

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

Quick view
Full text preview
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

[1] S. Sekhar, W. Al-Nuaimy and A. K. Nandi, "Automated localisation of retinal optic disk using Hough transform", In Proc. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2008), Paris, France, 2008. pp. 1577-80.
[CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 92]

[2] D. Welfer and J. Scharcanski, C. M. Kitamura, M. M. Dal Pizzol, L. W. B. Ludwig, D. R. Marinho, "Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach", Comput Biol Med, vol. 40, no. 2, pp. 124-137, 2010.
[CrossRef] [Web of Science Times Cited 72] [SCOPUS Times Cited 102]

[3] M. Niemeijer, M. D. Abramoff and B. V. Ginneken, "Fast detection of the optic disc and fovea in color fundus photographs", Medical Image Analysis, vol. 13, no. 6, pp. 859-870, 2009.
[CrossRef] [Web of Science Times Cited 104] [SCOPUS Times Cited 136]

[4] C. Duanggate, B. Uyyanonvara, S. S. Makhanov, S. Barman and T. Williamson, "Parameter-free optic disc detection", Comput Med Imaging Graph, vol. 35, no. 1, pp. 51-63, 2011.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 20]

[5] H. F. Jelinek, C. Depardieu, C. Lucas, D. Cornforth, W. Huang and M. J. Cree, "Towards vessel characterisation in the vicinity of the optic disc in digital retinal images", in Proc. the image and vision computing conference, Otago, New Zealand, 2005.

[6] A. Osareh, M. Mirmehdi, B. Thomas and R. Markham, "Colour morphology and snakes for optic disc localisation", in Proc. the 6th medical image understanding and analysis conference, A. Houston and R. Zwiggelaar (editors), BMVA Press, pp. 21-24, 2002.

[7] D. Kavitha and D. S. Shenbaga, "Automatic detection of optic disc and exudates in retinal images", in Proc. IEEE Int. conf. on intelligent sensing and information processing (ICISIP 2005), pp. 501-506, 2005.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 41]

[8] K. W. Tobin, E. Chaum, V. P. Govindasamy, T. P. Karnowski and O. Sezer, "Characterization of the optic disc in retinal imagery using a probabilistic approach", in Proc. SPIE International Symposium on Medical Imaging, San Diego, California, USA, vol. 6144:61443F, 2006.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 25]

[9] P. C. Siddalingaswamy and P. K. Gopalakrishna, "Automatic Localization and Boundary Detection of Optic Disc Using Implicit Active Contours", International Journal of Computer Applications, vol. 1, no. 6, pp. 1-5, 2010.

[10] C. Köse, U. ªevik, C. Ikibaº and H. Erdöl, "Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images", Comput Methods Programs Biomed, vol. 107, no. 2, pp. 274-293, 2012.

[11] C. Muramatsu, T. Nakagawa, A. Sawada, Y. Hatanaka, T. Hara, T. Yamamoto and H. Fujita, "Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods", Comput Methods Programs Biomed, vol. 101, no. 1, pp. 23-32, 2011.
[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 74]

[12] H.-F. Ng, "Automatic thresholding for defect detection", Pattern Recogn Letters, vol. 27, no. 14, pp. 1644-1649, 2006.
[CrossRef] [Web of Science Times Cited 218] [SCOPUS Times Cited 292]

[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] [Web of Science Times Cited 13669]

[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.
[CrossRef] [Web of Science Times Cited 203] [SCOPUS Times Cited 318]

[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 13]

[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.
[CrossRef] [Web of Science Times Cited 69] [SCOPUS Times Cited 86]

[22] S. Morales, V. Naranjo, J. Angulo and M. Alcaniz, "Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology", IEEE Trans Med Imaging, vol. 32, no. 4, pp. 786-796, 2013.
[CrossRef] [Web of Science Times Cited 78] [SCOPUS Times Cited 109]

[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 30] [SCOPUS Times Cited 35]

[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 7]

[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 320] [SCOPUS Times Cited 401]

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

[28] C. D. Manning, P. Raghavan and H. Schütze, "Introduction to Information Retrieval", Draft Online Copy (2009.04.01), Cambridge University Press, New York, NY, USA, 2009. [Online] Available: Temporary on-line reference link removed - see the PDF document

[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.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 49]

[30] B. Karasulu, "An Approach Based on Simulated Annealing to Optimize the Performance of Extraction of the Flower Region using Mean-Shift Segmentation", Applied Soft Computing, vol. 13, no. 12, pp. 4763-4785, 2013.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 9]

[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: 14,948 TCR
SCOPUS® Citations for all references: 1,809 TCR

Web of Science® Average Citations per reference: 467 ACR
SCOPUS® Average Citations per reference: 57 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 2019-02-21 11:02 in 141 seconds.

Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2019
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.

Website loading speed and performance optimization powered by: