Meridian-oriented abdomen acupoint detection and localization

Zhihao Feng; Yifan Deng; Qiang He

doi:10.1117/12.3044265

27 August 2024 Meridian-oriented abdomen acupoint detection and localization

Zhihao Feng, Yifan Deng, Qiang He

Author Affiliations +

Proceedings Volume 13252, Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024); 1325225 (2024) https://doi.org/10.1117/12.3044265
Event: 2024 Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), 2024, Kaifeng, China

Abstract

In Chinese acupuncture, moxibustion of the abdomen can energize the meridians, dissipate cold and relieve pain, promote water retention and reduce swelling, and warm the kidney and yang. The traditional abdominal acupoint identification, which mainly relies on the experience of doctors, is highly subjective and uncertain. To crack this problem, this paper proposes an abdominal meridian detection and abdominal acupoint location method based on geometric topological relations. First, we use MediaPipe to extract 33 human skeletal key points and YOLOv8 to detect the left and right nipples and navel, which are used as the reference points for detecting meridians and acupoints. Then the geometric topological relationships of the meridians and acupoints are established through the human body reference points, so as to determine the major meridians in the abdomen, such as the Conception Vessel Meridian, the Spleen Meridian, the Stomach Meridian, and the Sidney Meridian. Finally, under the guidance of the meridians, each acupoint in the abdomen was determined by the bone degree and cun method. The experimental results have showed that the method was able to accurately identify and localize the major 35 abdominal acupoints with an average error of less than 3 mm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhihao Feng, Yifan Deng, and Qiang He "Meridian-oriented abdomen acupoint detection and localization", Proc. SPIE 13252, Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), 1325225 (27 August 2024); https://doi.org/10.1117/12.3044265

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KEYWORDS

Abdomen

Deep learning

Spleen

Surface plasmons

Kidney

Stomach

Robots

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