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]  A. Elmoataz,et al.  Segmentation of cytological images using color and mathematical morphology , 1999 .

[2]  A. G. Ramakrishnan,et al.  Automation of differential blood count , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[3]  Vincenzo Piuri,et al.  All-IDB: The acute lymphoblastic leukemia image database for image processing , 2011, 2011 18th IEEE International Conference on Image Processing.

[4]  G. Zack,et al.  Automatic measurement of sister chromatid exchange frequency. , 1977, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[5]  F. Scotti,et al.  Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[6]  Joakim Lindblad,et al.  Development of Algorithms for Digital Image Cytometry , 2002 .

[7]  I. Cseke,et al.  A fast segmentation scheme for white blood cell images , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[8]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[9]  T. Leauhatong,et al.  A NEW WHITE BLOOD CELL SEGMENTATION USING MEAN SHIFT FILTER AND REGION GROWING ALGORITHM , 2011 .

[10]  Hubert Cardot,et al.  Cooperation of color pixel classification schemes and color watershed: a study for microscopic images , 2002, IEEE Trans. Image Process..

[11]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[12]  T Leauhatong,et al.  WHITE BLOOD CELL SEGMENTATION USING MEAN SHIFT ALGORITHM , 2011 .

[13]  Hamdan O. Alanazi,et al.  An Automated White Blood Cell Nucleus Localization and Segmentation using Image Arithmetic and Automatic Threshold , 2010 .