An AAM-Based Identification Method for Ear Acupoint Area
|Corresponding Author||Lu，Dongxin; Ke，Wende|
Ear image segmentation and identification is for the “observation” of TCM (traditional Chinese medicine), because disease diagnoses and treatment are achieved through the massaging of or pressing on some corresponding ear acupoints. With the image processing of ear image positioning and regional segmentation, the diagnosis and treatment of intelligent traditional Chinese medicine ear acupoints is improved. In order to popularize ear acupoint therapy, image processing technology has been adopted to detect the ear acupoint areas and help to gradually replace well-trained, experienced doctors. Due to the small area of the ear and the numerous ear acupoints, it is difficult to locate these acupoints based on traditional image recognition methods. An AAM (active appearance model)-based method for ear acupoint segmentation was proposed. The segmentation was illustrated as 91 feature points of a human ear image. In this process, the recognition effects of the ear acupoints, including the helix, antihelix, cymba conchae, cavum conchae, fossae helicis, fossae triangularis auriculae, tragus, antitragus, and earlobe, were divided precisely. Besides these, specially appointed acupoints or acupoint areas could be prominent in ear images. This method made it possible to partition and recognize the ear’s acupoints through computer image processing, and maybe own the same abilities as experienced doctors for observation. The method was proved to be effective and accurate in experiments and can be used for the intelligent diagnosis of diseases.
Public welfare technology research project of Zhejiang Provinces Science Foundation in China. The effect model Construction and 3D visualization of auricular point pivot regulation of brain neural[LGF20F020009] ; Key Ramp;D Program of Zhejiang Province. Research on intelligent service technology and equipment of health and elderly care-Support the research and application development of medical nursing care robot and elderly care service system of Internet hospita[2020C03107]
|WOS Research Area|
Engineering ; Materials Science
Engineering, Multidisciplinary ; Materials Science, Biomaterials
|WOS Accession No|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Mechanical and Energy Engineering|
1.Health Management System Engineering Center,School of Public Health,Hangzhou Normal University,Hangzhou,311121,China
2.Department of Mechanical and Energy Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Institute of Oceanographic Instrumentation,Qilu University of Technology (Shandong Academy of Sciences),Qingdao,266075,China
|Corresponding Author Affilication||Department of Mechanical and Energy Engineering|
Li，Qingfeng,Chen，Yuhan,Pang，Yijie,et al. An AAM-Based Identification Method for Ear Acupoint Area[J]. Biomimetics,2023,8(3).
Li，Qingfeng,Chen，Yuhan,Pang，Yijie,Kou，Lei,Lu，Dongxin,&Ke，Wende.(2023).An AAM-Based Identification Method for Ear Acupoint Area.Biomimetics,8(3).
Li，Qingfeng,et al."An AAM-Based Identification Method for Ear Acupoint Area".Biomimetics 8.3(2023).
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