The purpose of this paper was to develop a reliable body shape analysis approach based on cluster analysis, k. nearestneighbor( KNN), and multi-class support vector machine( MSVM). Firstly,a total of 357 Chinese men w...The purpose of this paper was to develop a reliable body shape analysis approach based on cluster analysis, k. nearestneighbor( KNN), and multi-class support vector machine( MSVM). Firstly,a total of 357 Chinese men were selected to make a dataset. Secondly, the experiences of these data were not accumulated to build general models. Five body angles were extracted as independent variables. Four clusters were the most efficient cluster number for our study. Finally,the accuracy of body classifications is compared between KNN and MSVM. In this study,the body classification framework was studied to transfer the body feature data to intuitive types. Moreover,the adaptive made-tomeasure( MTM) framework based on body classification was studied. The case demonstration and analysis show the effectiveness of the study.展开更多
先天性肌病是一组遗传性肌肉疾病,临床上以从新生儿开始的肌张力低和呼吸暂停为特征,并伴有特异的肌肉组织病理学特征。Spiro A J等[1]于1967年首次提出“肌管性肌病”(myotubular myopathy,MTM)一词。其中最严重的一种基因型被称为X-...先天性肌病是一组遗传性肌肉疾病,临床上以从新生儿开始的肌张力低和呼吸暂停为特征,并伴有特异的肌肉组织病理学特征。Spiro A J等[1]于1967年首次提出“肌管性肌病”(myotubular myopathy,MTM)一词。其中最严重的一种基因型被称为X-连锁肌管肌病(X-linked myotubular myopathy,XLMTM),通常伴有不同程度的张力减退和全身肌肉无力。X-连锁肌管肌病是由Xq28上的MTM1基因突变引起的[2]。它编码一种酪氨酸磷酸酶,该酶已被证明参与控制细胞生长的转导途径,并在维持肌紧张中起重要作用[3]。XLMTM患者表现为肌张力减退、呼吸衰竭、全身肌无力等临床特征,肌肉组织病理学上可见大量小而圆的肌纤维。XLMTM的大多数突变可以在外显子3、4、8、9、11和12区被检测到[2]。本文报道1例新生儿肌管性肌病,其外显子3区存在MTM1基因突变。展开更多
基金Talent Project of Xiamen University of Technology,China(No.90030617)
文摘The purpose of this paper was to develop a reliable body shape analysis approach based on cluster analysis, k. nearestneighbor( KNN), and multi-class support vector machine( MSVM). Firstly,a total of 357 Chinese men were selected to make a dataset. Secondly, the experiences of these data were not accumulated to build general models. Five body angles were extracted as independent variables. Four clusters were the most efficient cluster number for our study. Finally,the accuracy of body classifications is compared between KNN and MSVM. In this study,the body classification framework was studied to transfer the body feature data to intuitive types. Moreover,the adaptive made-tomeasure( MTM) framework based on body classification was studied. The case demonstration and analysis show the effectiveness of the study.
