White blood cell automatic counting system based on support vector machine

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4432 LNCS, Issue PART 2, 2007, Pages 318-326

  Markiewicz, Tomasz ^a   Osowski, Stanisław ^a,b   Mariańska, Bozena ^c  

^a WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

^b MILITARY UNIVERSITY OF TECHNOLOGY   (Poland)

^c Transfusion Medicine   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); MEDICAL IMAGING; PROBLEM SOLVING; STATISTICAL METHODS; SUPPORT VECTOR MACHINES;

AUTOMATIC SYSTEMS; CELL EXTRACTION; WHITE BLOOD CELLS;

BLOOD;

EID: 38049067184     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71629-7_36     Document Type: Conference Paper

References (16)

1. Beksac M., Beksac M. S., Tippi V. B., Dura H. A, Karakas M. U., Nurcakar, A.: An Artificial Inteligent Diagnostic System on Differential Recognition of Hematopietic Cells from Microscopic Images. Cytometry, 30 (1997), 145-150
2. Duda R. O., Hart P. E., Stork P.: Pattern Classification and Scene Analysis. Wiley N.Y. (2003)
3. Guyon I., Weston J., Barnhill S., Vapnik V.: Gene Selection for Cancer Classification Using Support Vector Machines. Machine Learning, 46 (2002), 389-422
4. Hsu C. W., Lin C. J.: A Comparison Methods for Multi Class Support Vector Machines. IEEE Trans. Neural Networks, 13 (2002), 415-425
5. Lewandowski K., Hellmann A.: Haematology Atlas, Multimedia Medical Publisher. Gdansk (2001)
6. Matlab user manual - Image processing toolbox, Math Works, Natick (1999)
7. Markiewicz T., Osowski S., Mariánská B., Moszczyński L.: Automatic Recognition of the Blood Cells of Myelogenous Leukemia Using SVM. IJCNN Montreal, (2005), 2496-2501
8. Osowski S., Markiewicz: Support Vector Machine for Recognition of White Blood Cell of Leukemia (in Camps-Vails G., Rojo-Alvarez L., Martinez-Ramon M. (Eds.), Kernel Methods in Bioengineering, Communication and Image Processing), Idea Group (2007), 81-108
9. Schölkopf B., Smola A.: Learning with Kernels, MIT Press, Cambridge, MA (2002)
10. Schurmann J.: Pattern Classification, a Unified View of Statistical and Neural Approaches. Wiley N. Y. (1996)
11. Soille P.: Morphological Image Analysis, Principles and Applications. Springer Berlin (2003)
12. Theera-Umpon N., Gader P.: System-level Training of Neural Networks for Counting White Blood Cells. IEEE Trans. SMS-C, 32 (2002), 48-53
13. Ushizima D., M., Lorena A. C., de Carvalho A. C. P.: Support Vector Machines Applied to White Blood Recognition. V Int. Conf. Hybrid Intelligent Systems, Rio de Janeiro (2005), 234-237
14. Vapnik V.: Statistical Learning Theory. Wiley N.Y. (1998)
15. Wagner T.: Texture Analysis (in Jahne B., Haussecker H., and Geisser P., (Eds.), Handbook of Computer Vision and Application), Academic Press (1999), 275-309
16. DiffMaster Octavia DM96, Cellavision AB, www.cellavision.com