Precise segmentation of white blood cells by using multispectral imaging analysis techniques

Proceedings - The 1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008

Volumn , Issue , 2008, Pages 491-494

  Wu, Qiongshui ^a   Zeng, Libo ^a   Zheng, Hong ^a   Guo, Ningning ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

Bone marrow image; Multispectral imaging analysis; Segmentation; SVM; White blood cells

Indexed keywords

BONE; CELLS; IMAGING TECHNIQUES; INTELLIGENT CONTROL; INTELLIGENT NETWORKS; INTELLIGENT SYSTEMS; SUPPORT VECTOR MACHINES; VECTORS;

BONE MARROW IMAGE; MULTISPECTRAL IMAGING ANALYSIS; SEGMENTATION; SVM; WHITE BLOOD CELLS;

BLOOD;

EID: 58049152110     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICINIS.2008.105     Document Type: Conference Paper

References (17)

1. Bennett, J.M., Catovsky, D., and Daniel, M.T., Proposals for the classification of the acute leukaemias. British Journal of Haematology, 1976. 33(4): p. 451-458.
2. Theera-Umpon, N., White blood cell segmentation and classification in microscopic bone marrow images, in Fuzzy Systems And Knowledge Discovery, Pt 2, Proceedings. 2005, Springer-Verlag Berlin: Berlin. p. 787-796.
3. Nilsson, B. and Heyden, A., Segmentation of complex cell clusters in microscopic images: Application to bone marrow samples. Cytometry Part A, 2005. 66A(1): p. 24-31.
4. Zhang, X.W., Song, J.Q., et al., Extraction of karyocytes and their components from microscopic bone marrow images based on regional color features. Pattern Recognition, 2004. 37(2): p. 351-361.
5. Montseny, E., Sobrevilla, P., and Romani, S., A fuzzy approach to white blood cells segmentation in color bone marrow images, in 2004 Ieee International Conference on Fuzzy Systems, Vols 1-3, Proceedings. 2004, Ieee: New York. p. 173-178.
6. Meschino, G.J. and Moler, E., Semiautomated image segmentation of bone marrow biopsies by texture features and mathematical morphology. Analytical And Quantitative Cytology And Histology, 2004. 26(1): p. 31-38.
7. Hengen, H., Spoor, S., and Pandit, M. Analysis of blood and bone marrow smears using digital image processing techniques. 2002. San Diego, CA, United States: The International Society for Optical Engineering.
8. Hardeberg, J.Y., Acquisition and Reproduction of Colour Images: Colorimetric and Multispectral approaches. 1999, Paris: Ecole Nationale Supérieure des Télécommunications.
9. Levenson, R.M. and Hoyt, C.C., Spectral imaging and microscopy. American Laboratory, 2000. 32(22): p. 26-33.
10. Wu Q.S.,Zeng, LB,et al., Analysis of blood and bone marrow smears using multispectral imaging analysis Techniques, in Medical Imaging 2005: Image Processing, Pt 1-3, edited by J. M. Fitzpatrick, J. M. Reinhardt, Proceedings of SPIE Vol. 5747 (SPIE, Bellingham, WA, 2005): 1872-1882.
11. Gunn, S.R., Support Vector Machines for Classification and Regression. 1998, Faculty of Engineering and Applied Science, Department of Electronics and Computer Science, University of Southampton.
12. Cortes, C. and Vapnik, V., Support-vector network. Machine Learning, 1995. 20: p. 273-297.
13. Cristianini, N. and Shawe-Taylor, J., An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. 2000, Cambridge: Cambridge University Press.
14. Vapnik, V., Statistical Learning Theory. 1998, New York: Wiley.
15. Friedman, J., Another approach to polychotomous classification. 1996, Department of Statistics, Stanford University.
16. Hsu, C.W. and Lin, C.J., A simple decomposition method for support vector machines. Machine Learning, 2002. 46(1-3): p. 291-314.
17. Lin, C.J., A Library for Support Vector Machines. 2003, http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html.