ITMO
ru/ ru

ISSN: 1023-5086

ru/

ISSN: 1023-5086

Scientific and technical

Opticheskii Zhurnal

A full-text English translation of the journal is published by Optica Publishing Group under the title “Journal of Optical Technology”

Article submission Подать статью
Больше информации Back

УДК: 004.932.2

Erythrometry method based on a modified Hough transform

For Russian citation (Opticheskii Zhurnal):

Жданов И.Н., Потапов А.С., Щербаков О.В. Метод эритрометрии на основе модифицированного преобразования Хафа // Оптический журнал. 2013. Т. 80. № 3. С. 97–100.

 

Zhdanov I.N., Potapov A.S., Shcherbakov O.V. Erythrometry method based on a modified Hough transform [in Russian] // Opticheskii Zhurnal. 2013. V. 80. № 3. P. 97–100.

For citation (Journal of Optical Technology):

I. N. Zhdanov, A. S. Potapov, and O. V. Shcherbakov, "Erythrometry method based on a modified Hough transform," Journal of Optical Technology. 80(3), 201-203 (2013). https://doi.org/10.1364/JOT.80.000201

Abstract:

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.

Keywords:

erythrometry, Hough transform, erythrocytes, Price–Jones curve

Acknowledgements:

This work was carried out with the financial support of the Ministry of Education and Science of the Russian Federation.

OCIS codes: 170.1530, 100.3008

References:

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).
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).
9. Z. Liu, H. Qiao, and L. Xu, “Multisets mixture learning-based ellipse detection,” Pattern Recogn. 39, 731 (2006).
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).