Automatic venipuncture insertion point recognition based on machine vision

Cheng-Ho Chen, Yun-Sheng Ye and Wen-Tung Hsu
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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

Journal of Advances in Technology and Engineering Research, 2018, vol. 4, issue 5, 186-190

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.

Keywords: Machine vision; Automatic venipuncture; Image processing (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:apb:jaterr:2018:p:186-190

DOI: 10.20474/jater-4.5.1

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