Proceedings of the 1st International Conference on Islam, Science and Technology, ICONISTECH 2019, 11-12 July 2019, Bandung, Indonesia.

Research Article

Digital Processing of Blood Cells Using Matlab

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  • @INPROCEEDINGS{10.4108/eai.11-7-2019.2297827,
        author={Nunik Destria Arianti and Azah Kamilah Muda and Norashikin  Ahmad and Neny  Rosmawarni and Fitri  Ramasita},
        title={Digital Processing of Blood Cells Using Matlab},
        proceedings={Proceedings of the 1st International Conference on Islam, Science and Technology, ICONISTECH 2019, 11-12 July 2019, Bandung, Indonesia.},
        publisher={EAI},
        proceedings_a={ICONISTECH},
        year={2021},
        month={1},
        keywords={blood cells image processing mathlab},
        doi={10.4108/eai.11-7-2019.2297827}
    }
    
  • Nunik Destria Arianti
    Azah Kamilah Muda
    Norashikin Ahmad
    Neny Rosmawarni
    Fitri Ramasita
    Year: 2021
    Digital Processing of Blood Cells Using Matlab
    ICONISTECH
    EAI
    DOI: 10.4108/eai.11-7-2019.2297827
Nunik Destria Arianti1,*, Azah Kamilah Muda1, Norashikin Ahmad1, Neny Rosmawarni2, Fitri Ramasita2
  • 1: Universiti Teknikal Malaysia Melaka, 75450 Melaka, Malaysia
  • 2: Department of Information Technology, National Institute of Science and Technology, Indonesia
*Contact email: P031810028@student.utem.edu.my

Abstract

At present, the process of separating normal and abnormal red blood cells was carried out through laboratory testing, which often requires a lot of time and cost. Therefore, this paper presents an abnormal red blood cell detection scheme to make it easier to diagnose a disease, speed up the identification process so that it saves time and money. Because without having to go through a chemical process, which processes one by one so that it slows down the time of identification and uses high costs, the method proposed in this paper was to calculate the number of normal and abnormal blood cell objects using the digital image processing method using Matlab. This study could not only determine normal and abnormal blood cells using a feature extraction method based on shape. The impact of this study was to create a system to separate blood cells that have collided before the process of detecting the form and calculation of the number of normal and abnormal red blood cell objects in an image of a blood cell.