The differential counting of white blood cell provides invaluable information to pathologist 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. The objective of the present study is to develop an automatic tool to identify and classify the white blood cells namely, lymphocytes, monocytes and neutrophil in digital microscopic images. We have proposed color based segmentation method and the geometric features extracted for each segment are used to identify and classify the different types of white blood cells. The experimental results are compared with the manual results obtained by the pathologist and demonstrate the efficacy of the proposed method.

1. Mohammad Hamghalam and Ahmad Ayatollahi, “Automatic Counting of Leukocytes in Giemsa-Stained Images of Peripheral Blood smear” IEEE International Conference on Digital Image Processing, pp. 13-16, 2009.
2. S. H. Rezatofighi, H. Soltanian-Zadeh, R. Sharifian and R. A. Zoroofi, “A New Approach to White Blood Cell Nucleus Segmentation Based on Gram-Schmidt Orthogonalization” IEEE International Conference on Digital Image Processing, pp. 107-111, 2009.
3. Saif Zahir, Rejaul Chowdhury, and Geoffrey W. Payne, “Automated Assessment of Erythrocyte Disorders Using Artifical Neural Network”, IEEE International Symposium on Signal Processing and Information Technology, 2006.
4. Silvia Halim, Timo R. Bretschneider, Yikun Li, Peter R. Preiser and Claudia Kuss, “Estimating Malaria Parasitaemia from Blood Smear Images, IEEE ICARCV 2006.
5. Refai, H., Li, L., Teague, T. K., and Naukam, R., “Automatic Count of hepatocytes in microscopic images” Proc. Int’l Conf. on Image Processing, 2, pp. 1101-1104, Sept 2003.
6. FANG Yi, ZHENG Chongxun, PAN Chen and LIU Li, “White Blood Cell Image Segmentation Using On-line Trained Neural Network”, Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.
7. P.S. Hiremath and Parashuram Bannigidad , Automatic Classification of Bacterial Cells in Digital Microscopic Images, ICDIP 2010, February 26-28, 2010 Singapore (Accepted).
8. http://en.wikipedia.org/wiki/White_blood_cell
9. F. Kurugollu, B. Sankur. A. E. Harmanci, "Color Image Segmentation using histogram multithresholding and fusion," Image and Vision computing 19, pp. 915-928, 2001.
10. Sawsan F. Bikhet, Ahmed M. Darwish Hany A. Tolba, and Samir I. Shaheen, "Segmentation and Classification of White Blood Cells " IEEE pp. 2259-2261, 2000.
11. Vincenzo Piuri, Fabio Scotti. Morphological Classification of Blood Leukocytes by Microscope images, 2004. IEEE International conference on computational Intelligence for Measurement Systems and Applications Boston. MD, USA, 14-16 July 2004.
12. Rastislav Lukac, Konstantinos, N. Plataniotis, Color Image Processing, Methods and Applications, CRC Press(2007)

White blood cells segmentation image analysis leukocytes lymphocyte monocyte neutrophil color segmentation