Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30576
White Blood Cells Identification and Counting from Microscopic Blood Image

Authors: Lorenzo Putzu, Cecilia Di Ruberto


The counting and analysis of blood cells allows the evaluation and diagnosis of a vast number of diseases. In particular, the analysis of white blood cells (WBCs) is a topic of great interest to hematologists. Nowadays the morphological analysis of blood cells is performed manually by skilled operators. This involves numerous drawbacks, such as slowness of the analysis and a nonstandard accuracy, dependent on the operator skills. In literature there are only few examples of automated systems in order to analyze the white blood cells, most of which only partial. This paper presents a complete and fully automatic method for white blood cells identification from microscopic images. The proposed method firstly individuates white blood cells from which, subsequently, nucleus and cytoplasm are extracted. The whole work has been developed using MATLAB environment, in particular the Image Processing Toolbox.

Keywords: Segmentation, Biomedical Image Processing, automatic detection, White blood cell analysis

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8384


[1] J. Cheewatanon, T. Leauhatong, S. Airpaiboon, and M. Sangwarasilp, A New White Blood Cell Segmentation Using Mean Shift Filter and Region Growing Algorithm, 2011.
[2] I. Cseke, A Fast Segmentation Scheme for White Blood Cell Images, 1992.
[3] R. Donida Labati, V. Piuri, F. Scotti, ALL-IDB: the Acute Lymphoblastic Leukemia Image DataBase for image processing, 2011.
[4] R. C. Gonzalez, R. E. Woods, Digital Image Processing, Prentice Hall Pearson Education, Inc.. New Jersey, USA, 2002.
[5] R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing Using MATLAB, Pearson Prentice Hall Pearson Education, Inc., New Jersey, USA, 2004.
[6] V. A. Kovalev, A. Y. Grigoriev, H. Ahn, Robust Recognition of White Blood Cell Images, 1996.
[7] O. Lezoray, H. Cardot, Cooperation of Color Pixel Classification Schemes and Color Watershed: a Study for Microscopic Images, 2002.
[8] O. Lezoray, A. Elmoataz, H. Cardot, M. Revenu, Segmentation of Cytological Images Using Color and Mathematical Morphology, 1999.
[9] J. Lindblad, Development of Algorithms for Digital Image Cytometry, 2002.
[10] H. T. Madhloom, S. A. Kareem , H. Ariffin, A. A. Zaidan, H. O. Alanazi, B. B. Zaidan An Automated White Blood Cell Nucleus Localization and Segmentation using Image Arithmetic and Automated Threshold, 2010.
[11] N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979.
[12] F. Scotti, Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images, 2006
[13] N. Sinha, A. G. Ramakrishnan, Automation of Differential Blood Count, 2003.
[14] G. Zack, W. Rogers, S. Latt, Automatic measurement of sister chromatid exchange frequency, 1977.