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Segmentation of RBC in Blood Smear Image using Discrete Shearlet Transform

by Krishna Prasad Palli, Sreenivasa Reddy Edara, ChandraSekharaiah K.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 6
Year of Publication: 2016
Authors: Krishna Prasad Palli, Sreenivasa Reddy Edara, ChandraSekharaiah K.
10.5120/ijca2016908756

Krishna Prasad Palli, Sreenivasa Reddy Edara, ChandraSekharaiah K. . Segmentation of RBC in Blood Smear Image using Discrete Shearlet Transform. International Journal of Computer Applications. 137, 6 ( March 2016), 1-4. DOI=10.5120/ijca2016908756

@article{ 10.5120/ijca2016908756,
author = { Krishna Prasad Palli, Sreenivasa Reddy Edara, ChandraSekharaiah K. },
title = { Segmentation of RBC in Blood Smear Image using Discrete Shearlet Transform },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 6 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number6/24276-2016908756/ },
doi = { 10.5120/ijca2016908756 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:38:28.368793+05:30
%A Krishna Prasad Palli
%A Sreenivasa Reddy Edara
%A ChandraSekharaiah K.
%T Segmentation of RBC in Blood Smear Image using Discrete Shearlet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 6
%P 1-4
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Blood cell analysis is a critical step in the process of disease analysis. Blood analysis process, usually done manually, has been automated to overcome the cumbersome task of RBC identification, segmentation, and classification. The digital image processing techniques developed in the past few years has made the automation possible. Among all the levels of medical imaging of the blood cells, the segmentation of the blood cell is the most vital task. This paper attempts to develop a new technique to segment an RBC from blood smear images. The proposed method is implemented by extracting a color image from the light microscopic smear image. A green channel is extracted from the color image. Further, image filtering is used to remove noise from the captured image. Finally Red Blood Cell segmentation is implemented using discrete shearlet transform. The proposed method is tested on blood cell images.

References
  1. RitterNand Cooper J (2007) Segmentation and border identification of cells in images of peripheral blood smear slides. In Proceedings of the Thirtieth Australasian Conference on Computer Science 62:161–169
  2. J. M. Sarif, M. Miswan, M. Ngadi, M. S. H. Salam, and M. Mahadi bin Abdul Jamil, "Red blood cell segmentation using masking and watershed algorithm: A preliminary study in Biomedical Engineering (ICoBE), 2012 International Conference on, 2012, pp. 258-262.
  3. Kumar BR, Joseph DK and Sreenivasan TV (2002) Teager energy based blood cell segmentation. Digital Signal Processing. DSP 2002 14th International Conference on, vol. 2:619–622
  4. C. Rongtai, W. QingXiang, Z. Rui, F. Lijuan, and R. Chengmei, "Red blood cell segmentation using Active Appearance Model," in Signal Processing (ICSP), 2012 IEEE 11th International Conference on, 2012, pp. 1641-1644.
  5. Venkatalakshmi, B. and K. Thilagavathi. Automatic red blood cell counting using Hough transforms, 2013 IEEE Conference on Information & Communication Technologies (ICT). 2013.
  6. Sumeet Chourasiya, G Usha Rani (2014) “Automatic Red Blood Cell Counting using Watershed Segmentation” IJCSIT.
  7. Razali Tomari, Wan Nurshazwani Wan Zakaria, Mouhammad Mahadi Abdul Jamil, Faridah Mohd Nor, Nik Farhan Nik Faud, “Computer Aided System for Red Blood Cell Classification in Blood Smear Image” in International Conference on Robot PRIDE, Elsevier, 2014.
  8. Prof Samir K Bandyopadhyay, "Method for Blood Cell segmentation", JCRCS, vol. 2, No. 4, 2011.
  9. G. Easley, D. Labate, and W-Q. Lim “Sparse Directional Image Representations using the Discrete Shearlet Transform”, to appear in Appl. Comput. Harmon. Anal. 2008.
  10. K. Guo and D. Labate, “Optimally sparse multidimensional representation using shearlets”, SIAM J. Math. Anal., Vol.9, pp. 298–318, 2007.
  11. K. Guo and D. Labate, Characterization and analysis of edges using the continuous shearlet transform, SIAM J. Imaging Sciences 2 (2009), 959–986.
  12. K. Guo, D. Labate and W. Lim, Edge analysis and identification using the continuous shearlet transform, Appl. Comput. Harmon. Anal. 27 (2009), 24–46.
  13. S. Yi, D. Labate, G. R. Easley, and H. Krim, Edge detection and processing using shearlets, Proc. IEEE Int. Conference on Image Proc., San Diego, October 12-15, 2008.
  14. M. N. Do, and M. Vetterli, “The contourlet transform: an efficient directional multi-resolution image representation”, IEEE Trans. Image Process., vol. 14, no. 12, pp. 2091-2106, Dec. 2005.
  15. D. D. Po and M. N. Do, “Directional multiscale modeling of images using the contourlet transform”, IEEE Trans. Image Processing., vol. 15, no. 6, pp. 1610-1620, June 2006.
  16. W. Freeman and E. Adelson, “The design and use of steerable filters”, IEEE Trans. Patt. Anal. and Machine Intell., Vol. 13, pp. 891-906, 1991.
Index Terms

Computer Science
Information Sciences

Keywords

Discrete Shearlet transform median filtering Red Blood Cell Image Segmentation and Smear Image are the keywords used in the paper.

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