International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 1 - Issue 4, May 2012 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616


[Full Text]



Shraddha Shivhare, Rajesh Shrivastava



Automatic white blood cell classification, granulometric moments, mathematical morphology, pattern spectrum, white blood cell differential counts.



The differential counting of white blood cell provides invaluable information to doctors for diagnosis and treatment of many diseases. manually counting of white blood cell is a tiresome, time-consuming and susceptible to error procedure due to the tedious nature of this process, an automatic system is preferable. in this automatic process, segmentation and classification of white blood cell are the most important stages. An automatic segmentation technique for microscopic bone marrow white blood cell images is proposed in this paper. The segmentation technique segments each cell image into three regions, i.e., nucleus, cytoplasm, and background. In this paper, we investigate whether information about the nucleus alone is adequate to classify white blood cells. This is important because segmentation of nucleus is much easier than the segmentation of the entire cell, especially in the bone marrow where the white blood cell density is very high. Even though the boundaries between cell classes are not well-defined and there are classification variations among experts, we achieve a promising classification performance using neural networks with fivefold cross validation in which Bayes classifiers and artificial neural networks are applied as classifiers.The classification performances are evaluated by two evaluation measures: traditional and classwise classificationrates. we compare our results with other classifiers and previously proposed nucleus-based features. The results showthat the features using nucleus alone can be utilized to achieve aclassification rate of 77% on the test sets. Moreover, the classification performance is better in the class wise sense when the a priori information is suppressed in both the classifiers.



[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] Park, J. and Keller, J.M., Snakes on the watershed. Ieee Transactions On Pattern Analysis And Machine Intelligence, 2001. 23(10): p. 1201-1205.

[3] Keller, J.M., Gader, P.D., et al. Soft counting networks for bone marrow differentials. 2001: Systems, Man, and
Cybernetics, 2001 IEEE International Conference on.

[4] Jae-Sang, P. and Keller, J.M. Fuzzy patch label relaxation in bone marrow cell segmentation. in Systems, Man, and Cybernetics. 1997: IEEE International Conference on.

[5] Sobrevilla, P., Montseny, E., and Keller, J., White blood
cell detection in bone marrow images, in 18th International
Conference of the North American Fuzzy Information
Processing Society - Nafips, R.N. Dave and T. Sudkamp,
Editors. 1999, I E E E: New York. p. 403-407.

[6] 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.

[7] 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.

[8] 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.

[9] 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.

[10]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.

[11] 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.

[12] http://www.cri-inc.com.