1. Professor

2. Overview  Introduction  Segmentation  Histogram Analysis  Selection of Threshold Points  Measuring of Distances  Experimental Results  Conclusion

3. Introduction  Examination of Peripheral Blood Smear.  Counting of leukocytes in Giemsa-stained images.  Leukocyte count is used to determine the presence of an infection in the human body.  Here they used histogram of images and intensity of red cells which are major objects in images to select appropriate point for thresholding.

4. Blood Cells  Red Cells (erythrocyte)  Blood Platelets  White Blood Cells (leukocyte) 1) Neutrophil 2) Eosinophil 3) Basophil 4) Monocyte 5) Lymphocyte

5. Blood Cells NEUTROPHIL EOSINOPHIL BASOPHIL LYMPHOCYTEMONOCYTE ERYTHROCYTE

6. Methods for Complete Blood Count Two types  Manual  Automated

7. Manual (Spectrometry)  Used for determining hemoglobin concentration in whole blood.  The instrument used is spectrophonometer.  This measures monochromatic light transmitted through a solution to determine the concentration of the light absorbing substance in that solution.

8. Automated Two types 1. For determining hemoglobin concentration in whole blood. CELL-DYN 3200 2. Counting different blood cells ( WBC, RBC, Platelets)

9. Segmentation Cell segmentation is the process of identifying, then extracting cells from background. Three major categories are:  Boundary based  Region based  Thresholding

10. Histogram Analysis  An image histogram is a chart that shows the distribution of intensities in an indexed/intensity image.  Used to enhance the contrast between cells and the background.  Choose an appropriate point for thresholding.  For this, images must be stained and Giemsa-stain is used.

11. Figure 1. A typical image of peripheral blood smears with Giemsa stain. Figure 2. Histogram of Fig.1.

12. Figure 3. (a) Binary image after thresholding (b) Removed noisy object image.

13. Measuring of distance  In neutrophils the nucleus is frequently multilobed.  After thresholding merge these segmented nucleus.  Distances among nuclei have been calculated.  Merge the nuclei which those distances are less than the diameter of one leukocyte.

14. Figure 4. (a) Image of Neutrophils (b) Thresholded image

15. Operators Used  Erosion  Dilation

16. Figure 5. Result of dilation.

17. Experimental Results  The image data set contains 30 microscopic images of blood smear.  Images are taken by an electronic microscope with digital camera.  The accuracy of this method is nearly 96.7 %.  The resolution of images is 600×473 pixels.

18. Advantages  In labs hematologists analyze blood by microscope, it is tedious to locate and count cells. Thus this process is very helpful and necessary as it is easy and takes less time.  Histogram analysis used in this paper is robust to differences in staining.

19. Cont…  Effective and reliable as compared to other conventional methods.  Higher accuracy and better resolution of images.

20. Conclusion  Proposed a new detection algorithm based on histogram analysis.  Measurement of distance among nuclei.  Can detect almost all WBC in Giemsa-stained images of peripheral blood smear.

21. Reference [1] Saif Zahir, Rejaul Chowdhury, and Geoffrey W.Payne, “Automated Assessment of Erythrocyte Disorders Using Artificial Neural Network”, IEEE International Symposium on Signal Processing and Information Technology, 2006. [2] Silvia Halim, Timo R. Bretschneider, Yikun Li, Peter R. Preiser and Claudia Kuss, “Estimating Malaria Parasitaemia from Blood Smear Images, IEEE ICARCV 2006. [3] Refai, H., Li, L., Teague, T.K., and Naukam, R., “Automatic count of hepatocytes in microscopic images,” Proceedings of the International Conference on Image Processing, 2, pp. 1101–1104, September 2003. [4] 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.

22. Cont… [5] C.Ruberto, A.Dempster, S.Khan and B.Jarra, "Analysis of blood cell images using morphological operators," Image and Vision Computing., vol.20, pp.133-146, 2002. [6] Q.Liano, and Y.Deng, "An Accurate Segmentation Method for White Blood Cell Images," IEEE Conf. Biomedical Imaging, pp.245-248, 2002. [7] N. Otsu, “A threshold selection method from graylevel histograms,” IEEE Transactions on Systems, Man, and Cybernetics 9(1), pp. 62–66, 1979. [8] K. Wu, D. Gauthier, and M. Levine, “Live cell image segmentation,” IEEE Transactions on Biomedical Engineering 42(1), pp. 1–12, 1995. [9] T. Markiewicz, S. Osowski, L. Moszczyski, and R. Satat1, “Myelogenous leukemia cell image preprocessing for feature generation,” in 5th International Workshop on Computational Methods in Electrical Engineering, pp. 70–73, 2003.