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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 895267, 15 pages
http://dx.doi.org/10.1155/2015/895267
Research Article

Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques

1School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa
2School of Engineering, University of KwaZulu-Natal, Durban 4000, South Africa

Received 8 September 2014; Accepted 13 November 2014

Academic Editor: Chuangyin Dang

Copyright © 2015 Temitope Mapayi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [9 citations]

The following is the list of published articles that have cited the current article.

  • Sourav Saha, Ankita Mandal, Sayantan Ganguly, Shreyan Giri, Priyodarshini Mondal, and Priya Ranjan Sinha Mahapatra, “A shape characterization framework for retinal vascular structure analysis,” 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 526–530, . View at Publisher · View at Google Scholar
  • Temitope Mapayi, and Jules-Raymond Tapamo, “Difference image and fuzzy c-means for detection of retinal vessels,” 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 169–172, . View at Publisher · View at Google Scholar
  • Temitope Mapayi, and Jules-Raymond Tapamo, “SAHF: Unsupervised texture-based multiscale with multicolor method for retinal vessel delineation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10072, pp. 639–648, 2016. View at Publisher · View at Google Scholar
  • Jules R. Tapamo, Adedayo O. Adio, Temitope Mapayi, and Serestina Viriri, “Automatic retinal vessel detection and tortuosity measurement,” Image Analysis and Stereology, vol. 35, no. 2, pp. 117–135, 2016. View at Publisher · View at Google Scholar
  • Jyotiprava Dash, and Nilamani Bhoi, “Detection of retinal blood vessels from ophthalmoscope images using morphological approach,” Electronic Letters on Computer Vision and Image Analysis, vol. 16, no. 1, pp. 1–14, 2017. View at Publisher · View at Google Scholar
  • Chetan L Srinidhi, P Aparna, and Jeny Rajan, “Recent Advancements in Retinal Vessel Segmentation,” Journal of Medical Systems, vol. 41, no. 4, 2017. View at Publisher · View at Google Scholar
  • Sarika B. Patil, Abbhilasha S. Narote, and Sandipann P. Narote, “Efficient retinal vessel detection using line detectors with morphological operations,” Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2829–2836, 2017. View at Publisher · View at Google Scholar
  • Ali Mohammad Alqudah, Hiam Alquraan, Isam Abu-Qasmieh, and Alaa Al-Badarneh, “Employing Image Processing Techniques and Artificial Intelligence for Automated Eye Diagnosis Using Digital Eye Fundus Images,” Journal of Biomimetics, Biomaterials and Biomedical Engineering, vol. 39, pp. 40–56, 2018. View at Publisher · View at Google Scholar
  • Nogol Memari, Abd Rahman Ramli, M. Iqbal Bin Saripan, Syamsiah Mashohor, and Mehrdad Moghbel, “Retinal Blood Vessel Segmentation by Using Matched Filtering and Fuzzy C-means Clustering with Integrated Level Set Method for Diabetic Retinopathy Assessment,” Journal of Medical and Biological Engineering, 2018. View at Publisher · View at Google Scholar