Internal bleeding detection algorithm based on determination of organ boundary by low-brightness set analysis

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    This paper proposes an organ boundary determination method for detecting internal bleeding. Focused assessment with sonography for trauma (FAST) is important for patients who are sent into shock by internal bleeding. However, the FAST has a low sensitivity, approximately 42.7 %, and delays of lifesaving treatment due to internal bleeding being missed have become a serious problem in emergency medical care. This study aims, therefore, to construct an automatic internal bleeding detection robotic system on the basis of ultrasound (US) image processing to improve the sensitivity. Internal bleeding has two key features: it is extracted from low-brightness areas in US images and accumulates between organs. We developed method for extracting low-brightness areas and determining algorithms of organ boundaries by low-brightness set analysis, and we detect internal bleeding by combining these two methods. Experimental results based on clinical US images of internal bleeding between Liver and Kidney showed that proposed algorithms had a sensitivity of 77.8% and specificity of 95.7%.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Intelligent Robots and Systems
    Pages4131-4136
    Number of pages6
    DOIs
    Publication statusPublished - 2012
    Event25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012 - Vilamoura, Algarve
    Duration: 2012 Oct 72012 Oct 12

    Other

    Other25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
    CityVilamoura, Algarve
    Period12/10/712/10/12

    Fingerprint

    Ultrasonography
    Luminance
    Ultrasonics
    Health care
    Liver
    Robotics
    Image processing

    Keywords

    • Emergency Medical Care
    • Image processing
    • Visual Feedback
    • Wearable system

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Software
    • Computer Vision and Pattern Recognition
    • Computer Science Applications

    Cite this

    Internal bleeding detection algorithm based on determination of organ boundary by low-brightness set analysis. / Ito, Keiichiro; Sugano, Shigeki; Iwata, Hiroyasu.

    IEEE International Conference on Intelligent Robots and Systems. 2012. p. 4131-4136 6385745.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Ito, K, Sugano, S & Iwata, H 2012, Internal bleeding detection algorithm based on determination of organ boundary by low-brightness set analysis. in IEEE International Conference on Intelligent Robots and Systems., 6385745, pp. 4131-4136, 25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012, Vilamoura, Algarve, 12/10/7. https://doi.org/10.1109/IROS.2012.6385745
    Ito, Keiichiro ; Sugano, Shigeki ; Iwata, Hiroyasu. / Internal bleeding detection algorithm based on determination of organ boundary by low-brightness set analysis. IEEE International Conference on Intelligent Robots and Systems. 2012. pp. 4131-4136
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    abstract = "This paper proposes an organ boundary determination method for detecting internal bleeding. Focused assessment with sonography for trauma (FAST) is important for patients who are sent into shock by internal bleeding. However, the FAST has a low sensitivity, approximately 42.7 {\%}, and delays of lifesaving treatment due to internal bleeding being missed have become a serious problem in emergency medical care. This study aims, therefore, to construct an automatic internal bleeding detection robotic system on the basis of ultrasound (US) image processing to improve the sensitivity. Internal bleeding has two key features: it is extracted from low-brightness areas in US images and accumulates between organs. We developed method for extracting low-brightness areas and determining algorithms of organ boundaries by low-brightness set analysis, and we detect internal bleeding by combining these two methods. Experimental results based on clinical US images of internal bleeding between Liver and Kidney showed that proposed algorithms had a sensitivity of 77.8{\%} and specificity of 95.7{\%}.",
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