TY - GEN
T1 - An improved hybrid strategy combining genetic simulated annealing algorithm and EBP used for image segmentation of test strip
AU - Wang, Jiajia
AU - Chen, Xiaozhu
AU - Tan, Jin
AU - Wang, Yaqun
PY - 2010
Y1 - 2010
N2 - In this paper, a type of improved hybrid strategy is used to carry out the image segmentation of gold immunochromatographic test strip, which combines the genetic simulated annealing algorithms (GSAAs) and error back propagation (EBP) neural network algorithm. This hybrid strategy can work out these problems in image segmentation of gold immunochromatographic test strip: the area of test strip is pretty small; the breadth of test line testing zone, control line testing zone will not be fixed due to the reaction between samples and test strip. Computer simulation on this hybrid strategy is relized by Visual C++. The results of computer simulation demonstrate that, comparing with the EBP algorithm, the convergence precision and training speed of the EBP neural network can be improved due to the parameters produced from GSAA. Furthermore, comparing with EBP algorithm, the convergence precision and convergence speed of GSAA is much better. At the end of this paper, this hybrid strategy is applied to image segmentation of test strip and get satisfactory effect.
AB - In this paper, a type of improved hybrid strategy is used to carry out the image segmentation of gold immunochromatographic test strip, which combines the genetic simulated annealing algorithms (GSAAs) and error back propagation (EBP) neural network algorithm. This hybrid strategy can work out these problems in image segmentation of gold immunochromatographic test strip: the area of test strip is pretty small; the breadth of test line testing zone, control line testing zone will not be fixed due to the reaction between samples and test strip. Computer simulation on this hybrid strategy is relized by Visual C++. The results of computer simulation demonstrate that, comparing with the EBP algorithm, the convergence precision and training speed of the EBP neural network can be improved due to the parameters produced from GSAA. Furthermore, comparing with EBP algorithm, the convergence precision and convergence speed of GSAA is much better. At the end of this paper, this hybrid strategy is applied to image segmentation of test strip and get satisfactory effect.
KW - EBP neutral network
KW - Genetic algorithm
KW - Gold immunochromatographic test strip
KW - Image segmentation
KW - Simulated annealing algorithm
UR - http://www.scopus.com/inward/record.url?scp=78649538713&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649538713&partnerID=8YFLogxK
U2 - 10.1109/ICCASM.2010.5620535
DO - 10.1109/ICCASM.2010.5620535
M3 - Conference contribution
AN - SCOPUS:78649538713
SN - 9781424472369
T3 - ICCASM 2010 - 2010 International Conference on Computer Application and System Modeling, Proceedings
SP - V419-V423
BT - ICCASM 2010 - 2010 International Conference on Computer Application and System Modeling, Proceedings
T2 - 2010 International Conference on Computer Application and System Modeling, ICCASM 2010
Y2 - 22 October 2010 through 24 October 2010
ER -