Blood Cell Counting and Segmentation Using Image Processing Techniques
The accurate count of a patient’s blood cells is vital for successful diagnosis of a plethora of diseases. Current systems deployed in Pakistan either rely on heavy and expensive machinery or is sometimes conducted manually. We propose the use of digital image processing techniques to build a cheaper alternative, that rely on digital images of blood smears, which are economical to produce, and are in fact a costless feature built-in to most existing lab microscopes. In this work, morphological image processing is deployed to segment the image and to differentiate and extract the blood cells from the plasma. The algorithm will exploit the shape and radius of blood cells for counting. After segmenting blood cells, their counting becomes a trivial task. The proposed system will be complementary to medical practitioners and provide a second opinion for their subjective diagnosis.
KeywordsComplete blood count Segmentation Image processing Watershed Overlapping cells
- 2.Visconte, V., Tabarroki, A., Gerace, C., Al-Issa, K., Hsi, E. D., Silver, B. J., Lichtin, A. E., & Tiu, R. V. (2014). Somatic mutations in splicing factor 3b, subunit 1 (SF3B1) are a useful biomarker differentiate between clonal and non-clonal causes of sideroblastic anemia. Blood, 124, 5597.Google Scholar
- 5.Aroon Kamath, M. (2014). Automated blood-cell analyzers. Can you count on them to count well?. Doctors Lounge Website. Available at: https://www.doctorslounge.com/index.php/blogs/page/17172
- 6.Mahmood, N. H., & Mansor, M. A. (2012). Red blood cells estimation using Hough transform technique. Signal & Image Processing, 3(2), 53.Google Scholar
- 7.Sharif, J. M., Miswan, M. F., Ngadi, M. A., Salam, M. S. H., & Bin Abdul Jamil, M. M. (2012). Red blood cell segmentation using masking and watershed algorithm: A preliminary study. In Biomedical Engineering (ICoBE), 2012 International Conference on (pp. 258-262). IEEE.Google Scholar
- 8.Bala, S., & Doegar, A. (2015). Automatic detection of sickle cell in red blood cell using watershed segmentation. International Journal of Advanced Research in Computer and Communication Engineering, 4(6), 488–491.Google Scholar
- 9.Joost Vromen, B. M. (2009). Red blood cell segmentation from SEM images. In: Image and Vision Computing New Zealand, (2009). IVCNZ’09. 24th International Conference, New Zealand.Google Scholar
- 10.Damahe, L. B., Krishna, R., & Janwe, N. (2011). Segmentation based approach to detect parasites and RBCs in blood cell images. International Journal of Computer Science and Applications, 4, 71–81.Google Scholar
- 15.Jiang, K., Liao, Q. M., & Dai, S. Y. (2003). A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering. In Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (pp. 2820–2825). IEEE.Google Scholar
- 17.Acharjya, P. P., Sinha, A., Sarkar, S., Dey, S., & Ghosh, S. (2013). A new approach of watershed algorithm using distance transform applied to image segmentation. International Journal of Innovative Research in Computer and Communication Engineering, 1(2), 185–189.Google Scholar
- 18.Kakarla, J., & Majhi, B. (2013). A new optimal delay and energy efficient coordination algorithm for WSAN. In 2013 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (pp. 1–6).Google Scholar
- 20.Mazalan, S. M. Automated red blood cells counting in peripheral blood smear image using Circular Hough Transform. In: Artificial intelligence, modelling and simulation (AIMS), first international conference on artificial intelligence, modelling & simulation, IEEE, (2013).Google Scholar
- 22.Tcheslavski, G. V. (n.d.). Morphological image processing: Basic algorithms. Retrieved from http://ee.lamar.edu/gleb/dip/ (7/05/2016).