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Detection and Counting of Red Blood Cells in Blood Cell Images using Hough Transform

by Mausumi Maitra, Rahul Kumar Gupta, Manali Mukherjee
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 53 - Number 16
Year of Publication: 2012
Authors: Mausumi Maitra, Rahul Kumar Gupta, Manali Mukherjee
10.5120/8505-2274

Mausumi Maitra, Rahul Kumar Gupta, Manali Mukherjee . Detection and Counting of Red Blood Cells in Blood Cell Images using Hough Transform. International Journal of Computer Applications. 53, 16 ( September 2012), 13-17. DOI=10.5120/8505-2274

@article{ 10.5120/8505-2274,
author = { Mausumi Maitra, Rahul Kumar Gupta, Manali Mukherjee },
title = { Detection and Counting of Red Blood Cells in Blood Cell Images using Hough Transform },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 53 },
number = { 16 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume53/number16/8505-2274/ },
doi = { 10.5120/8505-2274 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:54:15.797488+05:30
%A Mausumi Maitra
%A Rahul Kumar Gupta
%A Manali Mukherjee
%T Detection and Counting of Red Blood Cells in Blood Cell Images using Hough Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 53
%N 16
%P 13-17
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Counting of red blood cells (rbc) in blood cell images is very important to detect as well as to follow the process of treatment of many diseases like anaemia, leukaemia etc. However, locating, identifying and counting of -red blood cells manually are tedious and time-consuming that could be simplified by means of automatic analysis, in which segmentation is a crucial step. In this paper, we present an approach to automatic segmentation and counting of red blood cells in microscopic blood cell images using Hough Transform. Detection and counting of rbc have been done on five microscopic images and finally discussion has been made by comparing the results achieved by the proposed method and the conventional manual counting method.

References
  1. Lehmann T. M. , Wein B. , Dahmen J. , Bredno J. , Vogelsang F. & Kohnen M. : Content based image retrieval in medical applications : a novel multi step approach. International Society for Optical Engineering (SPIE), 3972, pp. 312-320. (2000)
  2. Dwi Anoragaingrum : Cell segmentation with median filter and mathematical morphology operation, proceeding of the IEEE 10th International Conference on Image Analysis and Processing (ICIAP), pp. 1043-1046 (1999).
  3. Keng Wu et al. : Live cell image segmentation, IEEE Trans on Biomedical Engineering, 42(1), pp. 1-12. (1995).
  4. Mark B. Jeacocke, Brian C. Lovell : A Multi-resolution algorithm for Cytological image segmentation, The second Australian and New Zealand conference on intelligent information systems, 322-326 (1994).
  5. Choi H, Baraniuk R. , Multiscale : Image segmentation using wavelet-domain hidden Markov models, IEEE Transaction on image processing, 10(9), pp. 1309-1321 (2001).
  6. S. Y. Cho, T. W. S. Cho and C. T. Leung, : A neural based crowd estimation by hybrid global learning algorithm, IEEE Transaction on Systems, Man andCybernetics, Part B, 29(4), pp. 535-541(1999).
  7. O. Barinova, V. Lempitsky and P. Kohli, : On the detection of multiple object instances using Hough Transforms , CVPR, (2010).
  8. D. Kong, D. Gray and H. Tao : A viewpoint invariant approach for crowd counting, ICPR (3), pp. 1187-1190 (2006).
  9. B. Leibe, A. Leonardis and B. Schiele, Robust object detection with interleaved categorization and segmentation, International journal of Computer Vision, 77(1), 2008, 259-289.
  10. A. N. Marana, S. A. Velastin, L. F. Costa and R. A. Lotufo, Estimation of crowd density using image processing, Image Processing for Security Applications, 1997, 1-8.
  11. D. Ryan, S. Denman, C. Fookes and S. Sridharan : Crowd counting usingmultiple local features, Proceedings of the Digital Image Computing: Techniques and Applications, pp. 81-88 (2009)
  12. V. Lempitsky and A. Zisserman, : Learning to count objects in images, CVPR, NIPS, (CMP Prague Colloquium) (2010).
  13. Abbott Diagnostics Website. http://www. abbott. com/products/diagnostics. htm/
  14. Beckman Coulter Website, http://www. coulter. com/coulter/Hematology/
  15. Vincenzo Piuri, Fabio Scotti : Morphological classification of blood leucocytes by microscope images, IEEE International conference on Computational Intelligence for Measurement Systems and Applications, pp. 103-108 (2004).
  16. P. V. C. Hough : Method and means of recognizing complex patterns, U. S. patent 3069654, 1962
  17. D. H. Baalard : Generalizing the Hough Transform to detect arbitrary shapes, Pattern Recognition, 13(2), pp. 111-122 (1981).
  18. R. O. Duda and P. E. Hart : Use of Hough Transformation to detect lines and curves in pictures, Comm. ACM, 15, pp 11-15 (1972).
  19. J. Gall and V. Lempitsky : Class Specific Hough forests for object detection, CVPR, (2009).
  20. R. Okada : Discriminative generalized Hough Transform for object detection, ICCV,( 2009).
  21. C. Gu, J. J. Lim, P. arbelaez and J. Malik : Recognition using regions, CVPR, (2009)
Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Detection Red Blood Cell Counting Hough Transform

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