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期刊信息
  • 主管单位:
  • 中国科学技术协会
  • 主办单位:
  • 中国仪器仪表学会、上海光学仪器研究所、中国光学学会工程光学专业委员会
  • 主    编:
  • 庄松林
  • 地    址:
  • 上海市军工路516号上海理工大学《光学仪器》编辑部
  • 邮政编码:
  • 200093
  • 联系电话:
  • 021-55270110
  • 电子邮件:
  • gxyq@usst.edu.cn
  • 国际标准刊号:
  • 1005-5630
  • 国内统一刊号:
  • 31-1504/TH
  • 邮发代号:
  • 单    价:
  • 15.00
  • 定    价:
  • 90.00
基于PCA和SVM算法的肝癌细胞显微后向散射光谱分类
Classification of micro-backscattering spectra of liver cancer cell based on PCA and SVM algorithm
投稿时间:2019-05-14  
DOI:10.3969/j.issn.1005-5630.2020.02.005
中文关键词:  后向散射光谱  细胞分类  主成分分析  支持向量机  肝癌细胞
英文关键词:back-scattering spectrum  cell classification  principal component analysis  support vector machine  hepatocellular carcinoma cell
基金项目:国家自然科学基金(61775140)
作者单位E-mail
王成 上海理工大学 生物医学光学与视光学研究所上海 200093  
史继毅 上海理工大学 生物医学光学与视光学研究所上海 200093  
郑刚 上海理工大学 生物医学光学与视光学研究所上海 200093  
项华中 上海理工大学 生物医学光学与视光学研究所上海 200093  
陈明慧 上海理工大学 生物医学光学与视光学研究所上海 200093  
张大伟 上海理工大学 上海市现代光学系统重点实验室上海 200093
上海理工大学 教育部光学仪器与系统工程研究中心上海 200093 
dwzhang@usst.edu.cn 
摘要点击次数: 2
全文下载次数: 3
中文摘要:
      为了实现对肝癌的早期实时和在体探测,基于前期搭建的光纤共聚焦后向散射(FCBS)光谱仪获取肝癌细胞的显微后向散射光谱,分别使用主成分分析(PCA)和支持向量机(SVM)两种算法,对获得的正常肝细胞株(L02)、低转移潜能肝癌细胞株(MHCC97-L)和高转移潜能肝癌细胞株(HCCLM3)三种细胞的后向散射光谱进行分类。使用PCA对获得的三种细胞光谱数据进行降维分析,得到的前两个主成分综合了全部信息的95.4% ,由主成分1和主成分2的得分图可以观察到,三种细胞在直观上有明显的区分;对同一数据集选取69例对象通过SVM机器学习算法训练分类模型,随机抽取50例作为训练集,19例作为预测集,最终分类的准确度达到了94.7%。实验结果表明:使用光纤共聚焦后向散射(FCBS)光谱仪获取的细胞显微后向散射光谱可以分别通过PCA和SVM对不同转移潜能的肝癌细胞进行自动分类,这将为研究活检提供必要的检测手段。
英文摘要:
      In order to realize the clinical detection of hepatocellular carcinoma (HCC) in vivo, real time and earlier, a normal liver cell line L02, a low-metastatic-potential hepatocellular carcinoma cell line MHCC97-L and a high-metastatic-potential hepatocellular carcinoma cell line HCCLM3 were measured, respectively, based on the established fiber confocal back scattering micro-spectrometer (FCBS). The principal component analysis (PCA) and the support vector machine (SVM) algorithm were used to classify the acquired spectrums, respectively. The PCA was used to study the spectrum in wavelength range of 500−900 nm. The first two of the principal components have taken 95.4% of the whole information; therefore, the three kinds of cell distribution were distinguished obviously on the scores diagram of principal component. 69 object data were chosen randomly to train the SVM classification model. 50 sets of these data were used as training sets and 19 sets were used as testing sets. The classification accuracy of the model has reached 94.7%. These results have indicated that the back-scattering micro-spectra of cells measured by fiber confocal back scattering micro-spectrometer (FCBS) combined PCA or SVM could classify liver cancer cells with different metastatic potential automatically. This will provide the necessary testing tools for the research of hepatocellular carcinoma cell in vivo and real time.
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