{"title":"Automated positioning of breast feature points for parameter extraction based on 3D point cloud","authors":"Zejun Zhong , Beibei Zhang , Bingfei Gu , Yue Sun","doi":"10.1016/j.ergon.2025.103759","DOIUrl":null,"url":null,"abstract":"<div><div>To achieve automatic extraction of parameter for female breast shape analysis, this paper proposed a “point-parameter-type” method based on 3D point cloud data. To standardize the measurement method, nine feature points (i.e., BBP, BP, FAP, FNP, LUBP, LBP, MBP, RUBP and UBP) and three lines (i.e., BBL, BL and BC) were firstly defined according to the characteristics of breast shape. Utilizing the 3D point cloud data, four positioning methods, including Max-Distance, Inflection-Points, Slope and Intersection-Point, were proposed to automate the positioning of feature points. Finally, breast morphological parameters for shape classification were calculated or predicted using computational models, and 140 subjects were randomly selected to verify the method accuracy. The results indicated that the recognition accuracy rates were 94.74 % for type FC, 89.06 % for type UO, and 89.47 % for type PO, demonstrating that this method is feasible. This study aims to establish a foundation for the automatic measurement of breast and provide valuable support for bra size recommendation for consumers during online shopping.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"107 ","pages":"Article 103759"},"PeriodicalIF":3.0000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Industrial Ergonomics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169814125000654","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
To achieve automatic extraction of parameter for female breast shape analysis, this paper proposed a “point-parameter-type” method based on 3D point cloud data. To standardize the measurement method, nine feature points (i.e., BBP, BP, FAP, FNP, LUBP, LBP, MBP, RUBP and UBP) and three lines (i.e., BBL, BL and BC) were firstly defined according to the characteristics of breast shape. Utilizing the 3D point cloud data, four positioning methods, including Max-Distance, Inflection-Points, Slope and Intersection-Point, were proposed to automate the positioning of feature points. Finally, breast morphological parameters for shape classification were calculated or predicted using computational models, and 140 subjects were randomly selected to verify the method accuracy. The results indicated that the recognition accuracy rates were 94.74 % for type FC, 89.06 % for type UO, and 89.47 % for type PO, demonstrating that this method is feasible. This study aims to establish a foundation for the automatic measurement of breast and provide valuable support for bra size recommendation for consumers during online shopping.
期刊介绍:
The journal publishes original contributions that add to our understanding of the role of humans in today systems and the interactions thereof with various system components. The journal typically covers the following areas: industrial and occupational ergonomics, design of systems, tools and equipment, human performance measurement and modeling, human productivity, humans in technologically complex systems, and safety. The focus of the articles includes basic theoretical advances, applications, case studies, new methodologies and procedures; and empirical studies.