Blood Cells Counting Using Modified Circular Hough Transform

  • Authors

    • Siti Madihah Mazalan
    • Khairul Huda Yusof
    • Nur Rashidah Ahmad Rashidi
    • Nasrul Humaimi Mahmood
    • Mohd Azhar Abdul Razak
    https://doi.org/10.14419/ijet.v8i1.12.28841
  • Blood cell, Modified Hough Transform, Image Processing, MatLab, Peripheral Blood Smear

  • The number, size and shape of blood cells are used to diagnose the various types of diseases such as leukemia, dengue, malaria and etc. Manual cell counting is a traditional method to count the number of cells and to acknowledge the state of a person’s health conditions based on the blood content. Problems using the manual cell counting under the microscope are time consuming and able to give errors. Therefore, we proposed a method to detect and determine the total number of blood cells by using Modified Hough transform (MHT) method. The blood cells image is analyzed using the developed algorithm in MatLab. In image processing, the process involves preprocessing and segmentation to find the radius range of cells. Then, MHT method is used to determine the number of blood cells based on the radius range of cells. Sixty samples of human blood cell image were tested and the accuracy is 94%

     

     

  • References

    1. [1] Miswan, M.F. et al., 2011. An Overview : Segmentation Method for Blood Cell Disorders. In N. A. A. Osman, ed. BIOMED 2011, IFMBE Proceeding 35. pp. 596–599.

      [2] Chen, Y., Chiang, P. & Cells, A.C., 2011. Automatic Cell Counting for Hemocytometers through Image Processing. World Academy of Science, Engineering and TEchnology, 58, pp.719–722.

      [3] Sharif, J.M. et al., 2012. Red Blood Cell Segmentation Using Masking and Watershed Algorithm : A Preliminary Study. Biomedical Engineering (ICoBE), 2012 International Conference, (February), pp.27–28.

      [4] Hiremath, P.S., Bannigidad, P. & Geeta, S., 2010. Automated Identification and Classification of White Blood Cells ( Leukocytes ) in Digital Microscopic Images. IJCA Special Issue on Recent Trends in Image Processing and Pattern Recognition (RTIPPR), 59.

      [5] Mahmood, N.H. & Mansor, M.A., 2012. Red Blood Cells Estimation using Hough Transform Technique. Signal & Image Processing: An International Journal (SIPIJ), 3(2), pp.53–64.

      [6] Maitra, M., Gupta, R.K. & Mukherjee, M., 2012. Detection and Counting of Red Blood Cells in Blood Cell Images using Hough Transform. International Journal of Computer Applications, 53(16), pp.18–22.

      [7] Chan, M., 2010. Cell detection for bee comb images using Circular Hough Transformation. 2010 International Conference on Science and Social Research (CSSR 2010), (Cssr), pp.191–195. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5773764.

      [8] Chen, J.J., Gao, Y.J. & Deng, Z.J., 2011. Counting of Microscopic Cells Based on Grads Hough Transform. Advanced Materials Research, 383-390, pp.7607–7612.

      [9] Cherabit, N., Chelali, F.Z. & Djeradi, A., 2012. Circular Hough Transform for Iris localization. Science and Technology, 2(5), pp.114–121.

      [10] Liangwongsan, S. et al., 2011. Extracted Circle Hough Transform and Circle Defect Detection Algorithm. World Academy of Science, Engineering and TEchnology, 60, pp.432–437.

      [11] Chitade, A.Z. & Katiyar, S.., 2010. Colour Based Image Segmentation using K-Means Clustering. International Journal of Engineering Science and Technology, 2(10), pp.5319–5325.

      [12] [Mohapatra, S., 2011. Automated Leukemia Detection in Blood Microscopic Images using Statistical Texture Analysis. ICCCS’11, pp.184–187.

      [13] Khashman, a, 2008. IBCIS: Intelligent blood cell identification system. Progress in Natural Science, 18(10), pp.1309–1314. Available at: http://linkinghub.elsevier.com/retrieve/pii/S1002007108002542 [Accessed September 13, 2012].

      [14] Khashman, A., 2009. Blood Cell Identification Using Emotional Neural Networks. Journal of Information Science and Engineering, 1751(25), pp.1737–1751.

      [15] Mao-jun, S. et al., 2008. A New Method for Blood Cell Image Segmentation and Counting Based on PCNN and Autowave. IEEE, (60572011), pp.6–9.

      [16] Hamghalam, M., Motameni, M. & Kelishomi, A.E., 2009. Leukocyte Segmentation in Giemsa-stained Image of Peripheral Blood Smears Based on Active Contour. 2009 International Conference on Signal Processing Systems, pp.103–106.

      [17] Mahmood, N.H. et al., 2012. Blood Cell Extraction using Color Based Segmentation Technique. International Journal of Life Science Biotechnology and Pharma Research, 2(2), pp.233–240.

  • Downloads

  • How to Cite

    Madihah Mazalan, S., Huda Yusof, K., Rashidah Ahmad Rashidi, N., Humaimi Mahmood, N., & Azhar Abdul Razak, M. (2019). Blood Cells Counting Using Modified Circular Hough Transform. International Journal of Engineering & Technology, 8(1.12), 36-41. https://doi.org/10.14419/ijet.v8i1.12.28841