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

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.

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

Blood Cells NEUTROPHIL EOSINOPHIL BASOPHIL LYMPHOCYTEMONOCYTE ERYTHROCYTE

Methods for Complete Blood Count Two types  Manual  Automated

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.

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

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

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.

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

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

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.

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

Operators Used  Erosion  Dilation

Figure 5. Result of dilation.

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.

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.

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

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.

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, [2] Silvia Halim, Timo R. Bretschneider, Yikun Li, Peter R. Preiser and Claudia Kuss, “Estimating Malaria Parasitaemia from Blood Smear Images, IEEE ICARCV [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 [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.

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 , [6] Q.Liano, and Y.Deng, "An Accurate Segmentation Method for White Blood Cell Images," IEEE Conf. Biomedical Imaging, pp , [7] N. Otsu, “A threshold selection method from graylevel histograms,” IEEE Transactions on Systems, Man, and Cybernetics 9(1), pp. 62–66, [8] K. Wu, D. Gauthier, and M. Levine, “Live cell image segmentation,” IEEE Transactions on Biomedical Engineering 42(1), pp. 1–12, [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.