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.

© 2013 Optical Society of America

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  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).

2012

A. Hamouda, A. Y. Khedr, and R. A. Ramadan, “Automated red blood cell counting,” Int. J. Comput. Sci. 1, No. 2, 13 (2012).

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).

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]

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).

2011

V. V. Kimbahune and N. J. Ukepp, “Blood cell image segmentation and counting,” Int. J. Eng. Sci. Technol. 3, 2448 (2011).

T. P. Nguyen and B. Kerautret, “Ellipse detection through decomposition of circular arcs and line segments,” Lect. Notes Comput. Sci. 6978, 554 (2011).
[CrossRef]

2006

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

C. Chang, “Detecting ellipses via bounding boxes,” Asian J. Health Inform. Sci. 1, No. 1, 73 (2006).

Ahuja, S.

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.

Basca, C. A.

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.

Brad, R.

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.

Chang, C.

C. Chang, “Detecting ellipses via bounding boxes,” Asian J. Health Inform. Sci. 1, No. 1, 73 (2006).

Dyrnaev, A. V.

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).

Gupta, R. K.

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).

Hamouda, A.

A. Hamouda, A. Y. Khedr, and R. A. Ramadan, “Automated red blood cell counting,” Int. J. Comput. Sci. 1, No. 2, 13 (2012).

Kerautret, B.

T. P. Nguyen and B. Kerautret, “Ellipse detection through decomposition of circular arcs and line segments,” Lect. Notes Comput. Sci. 6978, 554 (2011).
[CrossRef]

Khedr, A. Y.

A. Hamouda, A. Y. Khedr, and R. A. Ramadan, “Automated red blood cell counting,” Int. J. Comput. Sci. 1, No. 2, 13 (2012).

Kimbahune, V. V.

V. V. Kimbahune and N. J. Ukepp, “Blood cell image segmentation and counting,” Int. J. Eng. Sci. Technol. 3, 2448 (2011).

Liu, Z.

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

Maitra, M.

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).

Mukherjee, M.

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).

Nasution, A. M. T.

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.

Neatpisarnvanit, C.

J. Poomcokrak and C. Neatpisarnvanit, “Red blood cells extraction and counting,” in The Third International Symposium on Biomedical Engineering, 2008, pp. 199–203.

Nguyen, T. M.

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.

Nguyen, T. P.

T. P. Nguyen and B. Kerautret, “Ellipse detection through decomposition of circular arcs and line segments,” Lect. Notes Comput. Sci. 6978, 554 (2011).
[CrossRef]

Perumal, K.

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]

Ponuchamy, T.

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]

Poomcokrak, J.

J. Poomcokrak and C. Neatpisarnvanit, “Red blood cells extraction and counting,” in The Third International Symposium on Biomedical Engineering, 2008, pp. 199–203.

Potapov, A. S.

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).

Qiao, H.

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

Ramadan, R. A.

A. Hamouda, A. Y. Khedr, and R. A. Ramadan, “Automated red blood cell counting,” Int. J. Comput. Sci. 1, No. 2, 13 (2012).

Suryaningtyas, E. K.

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.

Talos, M.

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.

Ukepp, N. J.

V. V. Kimbahune and N. J. Ukepp, “Blood cell image segmentation and counting,” Int. J. Eng. Sci. Technol. 3, 2448 (2011).

Veluchamy, M.

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]

Wu, Q. M. J.

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.

Xu, L.

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

Am. J. Appl. Sci.

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]

Asian J. Health Inform. Sci.

C. Chang, “Detecting ellipses via bounding boxes,” Asian J. Health Inform. Sci. 1, No. 1, 73 (2006).

Int. J. Comput. Sci.

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).

A. Hamouda, A. Y. Khedr, and R. A. Ramadan, “Automated red blood cell counting,” Int. J. Comput. Sci. 1, No. 2, 13 (2012).

Int. J. Eng. Sci. Technol.

V. V. Kimbahune and N. J. Ukepp, “Blood cell image segmentation and counting,” Int. J. Eng. Sci. Technol. 3, 2448 (2011).

Lect. Notes Comput. Sci.

T. P. Nguyen and B. Kerautret, “Ellipse detection through decomposition of circular arcs and line segments,” Lect. Notes Comput. Sci. 6978, 554 (2011).
[CrossRef]

Nauchn. Tekhn. Vest. Informats. Tekhnol., Mekh. Opt.

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).

Pattern Recogn.

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

Other

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.

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.

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.

J. Poomcokrak and C. Neatpisarnvanit, “Red blood cells extraction and counting,” in The Third International Symposium on Biomedical Engineering, 2008, pp. 199–203.

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