Extraction and Recognition of Myeloma Cells in Microscopic Images of Bone Marrow Aspiration

Document Type : Original Article (s)

Authors

1 MSc Student, Department of Biomedical Engineering, School of Advanced Medical Technologies AND Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran

2 Associate Professor, Department of Pathology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

3 Associate Professor, Bioelectric and Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran

4 PhD Student, Department of Biomedical Engineering, School of Advanced Medical Technologies AND Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: Plasma cells are developed from B lymphocytes, a type of white blood cells generated in the bone marrow. The plasma cells produce antibodies to fight bacteria and viruses and stop infection and disease. In multiple myeloma, a cancer of plasma cells, collections of abnormal plasma cells (myeloma cells) accumulate in the bone marrow. Sometimes, existence of infection in body causes plasma cells increment, which could be diagnosed wrongly as multiple myeloma. Diagnosis of myeloma cells is mainly based on nucleus to cytoplasm ratio, compression of chromatin at nucleus, perinuclear zone in cytoplasm and etc.; so, because of depending final decision on human’s eye and opinion, error risk in decision may be occurred. In this study, we presented an automatic method using image-processing techniques for myeloma cells diagnosis from bone marrow smears.Methods: First, via contrast enhancement algorithm and k-means clustering, nucleus and cytoplasm of cells were completely extracted from bone marrow images. Then, for splitting connected nuclei and clump cells, two algorithms based on bottleneck and watershed methods were applied. Finally, via feature extraction from the nucleus and cytoplasm, myeloma cells were separated from normal plasma cells.Findings: The algorithm was applied on 30 digital images contained 64 normal plasma cells and 73 myeloma cells. Applying the automatic identification of myeloma cells on provided database showed the accuracy of 99.27%.Conclusion: In this study, an automatic method for detection and classification of plasma cells from myeloma cells in microscopic images of bone marrow aspiration was proposed.

Keywords

References

  1. Guyton CA. Textbook of medical physiology. 11th ed. Philadelphia, PA: Saunders; 2005.
  2. Minges Wols HA. Plasma cells. Hoboken, NJ: John Wiley and Sons; 2006.
  3. Mokhtar NR, Harun NH, Mashor MY, Roseline H, Mustafa N, Adollah R, et al. Image enhancement techniques using local, global, bright, dark and partial contrast stretching for acute leukemia images. World Congress on Engineering 2009; 1: 807.
  4. Soltanzadeh R, Rabbani H, Talebi A. Extraction of nucleolus candidate zone in white blood cells of peripheral blood smear images using curvelet transform. Computational and Mathematical Methods in Medicine 2012; 2012: 1-12.
  5. Chitade AZ, Katiyar S. Colour based image segmentation using k-means clustering. International Journal of Engineering Science and Technology 2010; 2(10): 5319-25.
  6. Ravichandran K, Ananthi B. Color skin segmentation using K-means cluster. International Journal of Computational and Applied Mathematics 2009; 4(2): 153-7.
  7. Gonzalez RC, Woods RE. Digital image orocessing. 2nd ed. Upper Saddle River, NJ: Prentice Hall; 2002. p. 299-300.
  8. Meyer F, Beucher S. Morphological segmentation. Journal of Visual Communication and Image Representation 1990; 1(1): 21-46.
  9. Wang H, Zhang H, Ray N. Clump splitting via bottleneck detection and shape classification. Pattern Recognition 2012; 45(7): 2780-7.
  10. Bala A. An Improved watershed image segmentation technique using MATLAB. International Journal of Scientific and Engineering Research 2012; 3(6): 1-4.
  11. Acharjya PP, Ghoshal D. A modified watershed segmentation algorithm using distances transform for image segmentation. International Journal of Computer Applications 2012; 52(12): 46-50.
  12. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2011; 2(3): 27.
  13. Smola A, Scholkopf B. A tutorial on support vector regression. Statistics and Computing 2004; 14(3): 199-222.
  14. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 2003; 56(11): 1129-35.
Journal of Isfahan Medical School
Volume 32, Issue 310 - Serial Number 310
September and October 2015
Pages 1976-1984

Files

History

  • Receive Date: 03 August 2014
  • Revise Date: 01 November 2024
  • Accept Date: 06 April 2022

Share

How to cite

Statistics