Application of support vector machine and genetic algorithm for improved blood cell recognition

IEEE Transactions on Instrumentation and Measurement

Volumn 58, Issue 7, 2009, Pages 2159-2168

  Osowski, Stanislaw ^a,b   Siroic, Robert ^a   Markiewicz, Tomasz ^c   Siwek, Krzysztof ^a  

^a WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

^b MILITARY UNIVERSITY OF TECHNOLOGY   (Poland)

^c MILITARY INSTITUTE OF MEDICINE   (Poland)

Author keywords

Blood cell recognition; Genetic algorithm (GA); Support vector machine (SVM)

Indexed keywords

AUTOMATIC RECOGNITION SYSTEM; BLOOD CELL RECOGNITION; BLOOD CELLS; BONE MARROW; DIAGNOSTIC FEATURES; MAIN TASKS; NUMERICAL VERIFICATION; SUPPORT VECTOR MACHINE (SVM); WHOLE SYSTEMS;

BLOOD; CELLS; IMAGE RETRIEVAL; SUPPORT VECTOR MACHINES; VECTORS;

GENETIC ALGORITHMS;

EID: 67649631501     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIM.2008.2006726     Document Type: Conference Paper

References (22)

1. J. M. Bennett, D. Catovsky, M. T. Daniel, G. Flandrin, D. A. Galton, H. R. Gralnick, and C. Sultan, "Proposals for the classification of the acute leukaemias. French-American-British (FAB) co-operative group," Br. J. Haematol., vol. 33, no. 4, pp. 451-458, Aug. 1976.
2. K. Lewandowski and A. Hellmann, Hematology Atlas. Gdansk, Poland: Multimedia Med., 2001.
3. M. Beksac, M. S. Beksac, V. B. Tippi, H. A. Duru, M. U. Karakas, and A. Nurcakar, "An artificial intelligent diagnostic system on differential recognition of hematopoietic cells from microscopic images," Cytom. vol. 30, no. 3, pp. 145-150, Jun. 1997.
4. N. Theera-Umpon and P. Gader, "System-level training of neural networks for counting white blood cells," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 32, no. 1, pp. 48-53, Feb. 2002.
5. S. Osowski and T. Markiewicz, "Support vector machine for recognition of white blood cells in leukemia," in Kernel Methods in Bioengineering, Signal and Image Processing, G. Camps-Valls, J. L. Rojo-Alvarez, and M. Martinez-Ramon, Eds. London, U.K.: Idea Group, 2007, pp. 93-123.
6. R. Siroic, S. Osowski, T. Markiewicz, and K. Siwek, "Support vector machine and genetic algorithm for efficient blood cell recognition," in Proc. IEEE IMTC, Warsaw, Poland, 2007, pp. 1-6.
7. B. Swolin, P. Simonsson, S. Backman, I. Lofqvist, I. Bredin, and M. Johnsson, "Differential counting of blood leukocytes using automated microscopy and a decision support system based on artificial neural networks - Evaluation of DiffMaster Octavia," Clin. Lab. Haematol. vol. 25, no. 3, pp. 139-147, Jun. 2003.
8. DiffMaster Octavia DM96, Cellavision AB. [Online]. Available: www. cellavision.com
9. R. O. Duda, P. E. Hart, and P. Stork, Pattern Classification and Scene Analysis. New York: Wiley, 2003.
10. J. Schurmann, Pattern Classification: A Unified View of Statistical and Neural Approaches. New York: Wiley, 1996.
11. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learn. vol. 46, no. 1-3, pp. 389-422, Jan. 2002.
12. P. Soille, Morphological Image Analysis, Principles and Applications Berlin, Germany: Springer-Verlag, 2003.
13. F. Scotti, "Robust segmentation and measurements techniques of white cells in blood microscope images," in Proc. IEEE IMTC, Sorrento, Italy, 2006, pp. 43-48.
14. Matlab User Manual, MathWorks, Natick, MA, 2004.
15. T. Wagner, "Texture analysis," in Handbook of Computer Vision and Application, B. Jahne, H. Haussecker, and P. Geisser, Eds. Boston, MA: Academic, 1999, pp. 275-309.
16. B. Schölkopf and A. Smola, Learning With Kernels. Cambridge, MA: MIT Press, 2002.
17. V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
18. L. Platt, "Fast training of SVM using sequential optimization," in Advances in Kernel Methods - Support Vector Learning, B. Scholkopf, B. Burges, and A. Smola, Eds. Cambridge, MA: MIT Press, 1998, pp. 185-208.
19. C.W. Hsu and C. J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415-425, Mar. 2002.
20. D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.
21. D. Ashlock, Evolutionary Computation for Modeling and Optimization Berlin, Germany: Springer-Verlag, 2006.
22. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. Berlin, Germany: Springer-Verlag, 1996.