White Blood Cells Identification and Counting from Microscopic Blood Image
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:49331", author = "Lorenzo Putzu and Cecilia Di Ruberto", title = "White Blood Cells Identification and Counting from Microscopic Blood Image", abstract = "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 = "Automatic detection, Biomedical image processing,
Segmentation, White blood cell analysis.", volume = "7", number = "1", pages = "1-8", }