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Target Area Extraction Algorithm for the In Vivo Fluorescence Imaging of Small Animals.
Zhang, Qiang; Wang, Lei; Qian, Qing; Wang, Jishuai; Cheng, Wenbo; Han, Kun.
Affiliation
  • Zhang Q; Academy for Engineering & Technology, Fudan University, Shanghai 200433, P. R. China.
  • Wang L; CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.
  • Qian Q; CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.
  • Wang J; CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.
  • Cheng W; CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.
  • Han K; CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.
ACS Omega ; 5(32): 20100-20106, 2020 Aug 18.
Article in En | MEDLINE | ID: mdl-32832764
ABSTRACT
Bio-optical imaging can noninvasively describe specific biochemical reaction events in small animals using endogenous or exogenous imaging reagents to label cells, proteins, or DNA. The fluorescence optical bio-imaging system excites the fluorescent group to a high energy state by excitation light and then generates emission light. However, many substances in the organism will also emit fluorescence after being excited by the excitation light, and the nonspecific fluorescence generated will affect the detection sensitivity. This paper designs and develops a set of high-level biosafety in vivo fluorescence imaging system for small animals suitable for virology research and proposes a target area extraction algorithm for fluorescence images. The fluorescence image target extraction algorithm first maps the nonlinear separation data in the low-dimensional space to the high-dimensional space. Then, based on the analysis of the characteristics of the fluorescent region, a method for discriminating the target fluorescent region based on the two-step entropy function is proposed, and the real target fluorescent region is obtained according to the set connected region. Based on the experiment of collecting and analyzing the in vivo fluorescent images of mice, it is verified that the proposed algorithm can automatically extract the target fluorescent region better than the classical linear model. It shows that the proposed algorithm is less affected by background fluorescence, and the estimated separated spectrum based on this method is closer to the real target spectrum.

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: ACS Omega Year: 2020 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: ACS Omega Year: 2020 Document type: Article