OSA's Digital Library

Journal of Optical Technology

Journal of Optical Technology


  • Vol. 80, Iss. 3 — Mar. 1, 2013
  • pp: 201–203

Erythrometry method based on a modified Hough transform

I. N. Zhdanov, A. S. Potapov, and O. V. Shcherbakov  »View Author Affiliations

Journal of Optical Technology, Vol. 80, Issue 3, pp. 201-203 (2013)

View Full Text Article

Acrobat PDF (263 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



This letter discusses the solution of the problem of automatic erythrometry, using a modified Hough transform based on a method developed earlier for distinguishing and counting erythrocytes. The proposed method makes it possible to construct a Price–Jones curve from the images of blood smears.

© 2013 Optical Society of America

OCIS Codes
(170.1530) Medical optics and biotechnology : Cell analysis
(100.3008) Image processing : Image recognition, algorithms and filters

Original Manuscript: December 19, 2012
Published: April 30, 2013

I. N. Zhdanov, A. S. Potapov, and O. V. Shcherbakov, "Erythrometry method based on a modified Hough transform," J. Opt. Technol. 80, 201-203 (2013)

Sort:  Author  |  Year  |  Journal  |  Reset


  1. M. Maitra, R. K. Gupta, and M. Mukherjee, “Detection and counting of red blood cells in blood cell images using Hough transform,” Int. J. Comput. Sci. 53, No. 16, 18 (2012).
  2. M. Veluchamy, K. Perumal, and T. Ponuchamy, “Feature extraction and classification of blood cells using artificial neural network,” Am. J. Appl. Sci. 9, 615 (2012). [CrossRef]
  3. J. Poomcokrak and C. Neatpisarnvanit, “Red blood cells extraction and counting,” in The Third International Symposium on Biomedical Engineering, 2008, pp. 199–203.
  4. V. V. Kimbahune and N. J. Ukepp, “Blood cell image segmentation and counting,” Int. J. Eng. Sci. Technol. 3, 2448 (2011).
  5. A. M. T. Nasution and E. K. Suryaningtyas, “Automated morphological processing for counting the number of red blood cell,” in Proceedings of the 2008 International Joint Conference in Engineering, Jakarta, Indonesia, August 4–5, 2008.
  6. A. Hamouda, A. Y. Khedr, and R. A. Ramadan, “Automated red blood cell counting,” Int. J. Comput. Sci. 1, No. 2, 13 (2012).
  7. T. M. Nguyen, S. Ahuja, and Q. M. J. Wu, “A real-time ellipse detection based on edge grouping,” in IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 3280–3286.
  8. T. P. Nguyen and B. Kerautret, “Ellipse detection through decomposition of circular arcs and line segments,” Lect. Notes Comput. Sci. 6978, 554 (2011). [CrossRef]
  9. Z. Liu, H. Qiao, and L. Xu, “Multisets mixture learning-based ellipse detection,” Pattern Recogn. 39, 731 (2006). [CrossRef]
  10. C. A. Basca, M. Talos, and R. Brad, “Randomized Hough transform for ellipse detection with result clustering,” in The International Conference on Computer as a Tool, EUROCON, 2005, vol. 2, pp. 1397–1400.
  11. C. Chang, “Detecting ellipses via bounding boxes,” Asian J. Health Inform. Sci. 1, No. 1, 73 (2006).
  12. A. V. Dyrnaev and A. S. Potapov, “Combined method of counting erythrocytes on images of blood smears,” Nauchn. Tekhn. Vest. Informats. Tekhnol., Mekh. Opt. 77, No. 1, 19 (2012).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article

OSA is a member of CrossRef.

CrossCheck Deposited