FEATURE FUSION-BASED FEATURE POINT EXTRACTION AND MATCHING OF THE THREE-DIMENSIONAL HUMAN VERTEBRAE MODEL

doi:10.1142/S0219519422400152

CQUET-IR

	FEATURE FUSION-BASED FEATURE POINT EXTRACTION AND MATCHING OF THE THREE-DIMENSIONAL HUMAN VERTEBRAE MODEL
	Hui, Yu ; Tang, Ji-Yong ; Ye, Kun ; Du, Jing ; Liu, Yu-Ke ; Huang, Wei
	2022-04
发表期刊	Journal of Mechanics in Medicine and Biology
ISSN	0219-5194
EISSN	1793-6810
卷号	22 期号:3
摘要	In the positioning and registration of a three-dimensional human vertebrae model, the main difficulty encountered is the extraction and matching of the model feature points. To accurately locate the vertebrae, it is necessary to optimize the existing feature point extraction and matching method. In this context, the present study was envisaged to achieve the purpose of accurate extraction and matching by integrating the features of curvature and Euclidean distance. It was found that this method removed the redundant point cloud data in the 3D model, thereby effectively improving the matching rate. Initially, the feature points were manually picked as the circle center, and the mean value ((H) over bar) of average curvature (H-i) of all points in the specified infinitesimal radius (r) sphere neighborhood were calculated. When H-i <= (H) over bar, it implied the point i tended to be flat relative to the whole point cloud surface, and was not regarded as a feature point. When H-i > (H) over bar, the point i exhibited a greater curvature relative to the whole point cloud surface, that is, a more prominent point. In this case, point i met the basic conditions for becoming a feature point and was included in the candidate feature point set. Subsequently, the Euclidean distance between the points in the candidate feature point set and the manually picked circle center points was calculated individually. The smaller the Euclidean distance between the two points, the higher the degree of feature similarity between the two points, and the stronger the matching of feature points. Therefore, the manually picked points were replaced with the candidate feature point with the closest distance that accurately reflected the change of the local geometric characteristics of the point. It was found that the accuracy of the improved method of the feature point extraction and matching was about 18% higher than that before the improvement, which verifies the effectiveness and robustness of the proposed method.
关键词	Human vertebrae three-dimensional model feature point extraction matching mean curvature Euclidean distance
DOI	10.1142/S0219519422400152
收录类别	SCIE
语种	英语
WOS研究方向	Biophysics ; Engineering
WOS类目	Biophysics ; Engineering, Biomedical
WOS记录号	WOS:000778127600003
出版者	WORLD SCIENTIFIC PUBL CO PTE LTD
原始文献类型	Article
出版地	SINGAPORE
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/13486
专题	重庆电子科技职业大学
作者单位	1.Hanshan Normal Univ, Sch Comp & Informat Engn, Chaozhou 521041, Peoples R China; 2.North Guangdong Peoples Hosp, Shaoguan 510525, Peoples R China; 3.Chongqing Elect Engn Vocat Coll, Sch Artificial Intelligence & Big Data, Chongqing 401331, Peoples R China; 4.West China Normal Univ, Sch Comp Sci, Nanchong 637002, Peoples R China
推荐引用方式 GB/T 7714	Hui, Yu,Tang, Ji-Yong,Ye, Kun,et al. FEATURE FUSION-BASED FEATURE POINT EXTRACTION AND MATCHING OF THE THREE-DIMENSIONAL HUMAN VERTEBRAE MODEL[J]. Journal of Mechanics in Medicine and Biology,2022,22(3).
APA	Hui, Yu,Tang, Ji-Yong,Ye, Kun,Du, Jing,Liu, Yu-Ke,&Huang, Wei.(2022).FEATURE FUSION-BASED FEATURE POINT EXTRACTION AND MATCHING OF THE THREE-DIMENSIONAL HUMAN VERTEBRAE MODEL.Journal of Mechanics in Medicine and Biology,22(3).
MLA	Hui, Yu,et al."FEATURE FUSION-BASED FEATURE POINT EXTRACTION AND MATCHING OF THE THREE-DIMENSIONAL HUMAN VERTEBRAE MODEL".Journal of Mechanics in Medicine and Biology 22.3(2022).