IDEAS home Printed from https://ideas.repec.org/a/apb/jaterr/2018p186-190.html
   My bibliography  Save this article

Automatic venipuncture insertion point recognition based on machine vision

Author

Listed:
  • Cheng-Ho Chen

    (Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan)

  • Yun-Sheng Ye

    (Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan)

  • Wen-Tung Hsu

    (Department of Pathology, Taichung Armed Forces General Hospital, Taichung, Taiwan)

Abstract

Venipuncture is a common practice performed in medical institutions. It now relies on well-trained medical staff. The work is inherently risky, requiring skills, experience, and a high degree of focus to avoid discomfort or even danger to the staff themselves or to the patients. The proper insertion point for venipuncture is sometimes dif- ficult to recognize. In recent years, many applications of machine vision and image processing technologies have been used to help physicians, nurses and other medical practitioners in determining the physical conditions of the patients, make the appropriate diagnosis, and reduce the fatigue or other human factors causing misdiagnosis. In this paper, the implement machine vision technologies to assist the recognition of venipuncture insertion point is studied. Two industrial CMOS cameras are used with an infrared light source. The two cameras are placed apart and tilted in a certain angle relative to each other in order to achieve stereo vision of the arm. Light filters are also installed on the lens of the two cameras. The cameras are calibrated beforehand to eliminate distortion. Two im- ages of the arm, one by each camera are captured. The images are then processed through image binarization and morphological algorithms. After image processing, the best needle insertion position, puncture depth and angle are determined. The developed system can improve the efficiency of venipuncture, and reduce the risk of medical staff and patients.

Suggested Citation

  • Cheng-Ho Chen & Yun-Sheng Ye & Wen-Tung Hsu, 2018. "Automatic venipuncture insertion point recognition based on machine vision," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 4(5), pages 186-190.
  • Handle: RePEc:apb:jaterr:2018:p:186-190
    DOI: 10.20474/jater-4.5.1
    as

    Download full text from publisher

    File URL: https://tafpublications.com/platform/Articles/full-jater4.5.1.php
    Download Restriction: no

    File URL: https://tafpublications.com/gip_content/paper/Jater-4.5.1.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.20474/jater-4.5.1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Salisu A. & Muktar, M. D. & Salisu, A. I. & Abdulhadi, Y. & M. Umar, 2017. "A Survey on the Prevalence of Hepatitis B Virus and Predisposing Factors among Blood Donors in Two General Hospitals in Jigawa State Nigeria," International Journal of Health and Medical Sciences, Mohammad A. H. Khan, vol. 3(2), pages 29-37.
    2. Faozia Ali S. Alsarori & Reza Hassanpour, 2016. "Automatic detection of breast cancer in mammogram images," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 2(6), pages 196-201.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. C. Camilleri & A. Rochman, 2019. "Defect formation in EBM parts built at different orientation," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 5(3), pages 125-134.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:apb:jaterr:2018:p:186-190. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: A/Professor Akbar A. Khatibi (email available below). General contact details of provider: https://tafpublications.com/platform/published_papers/10 .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.