An Improved Hybrid Strategy Combining Genetic Simulated Annealing Algorithm and EBP Used for Image Segmentation of Test Strip1
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.
image segmentation genetic algorithm simulated annealing algorithm gold immunochromatographic test strip EBP neutral network
Jiajia Wang Xiaozhu Chen Jin Tan Yaqun Wang
Department of Computer Science and Technology China Jiliang University Hangzhou, China
国际会议
太原
英文
19-23
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）