This paper describes a method to decompose multi-scale information from different source medical image using wavelet transformation. The data fusion between CT image and MRI image is implemented based on the coefficie...This paper describes a method to decompose multi-scale information from different source medical image using wavelet transformation. The data fusion between CT image and MRI image is implemented based on the coefficients fusion rule which included choice of regional variance and weighted average wavelet information. The result indicates that this method is better than WMF, LEF and RVF on fusion results, details and target distortion.展开更多
Dempster-Shafer(D-S)evidence theory is a key technology for integrating uncertain information from multiple sources.However,the combination rules can be paradoxical when the evidence seriously conflict with each other...Dempster-Shafer(D-S)evidence theory is a key technology for integrating uncertain information from multiple sources.However,the combination rules can be paradoxical when the evidence seriously conflict with each other.In the paper,we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise(DBSCAN)density clustering.In the proposed mechanism,firstly,the original evidence sets are preprocessed by DBSCAN density clustering,and a successfully focal element similarity criteria is used to mine the potential information between the evidence,and make a correct measure of the conflict evidence.Then,two different discount factors are adopted to revise the original evidence sets,based on the result of DBSCAN density clustering.Finally,we conduct the information fusion for the revised evidence sets by D-S combination rules.Simulation results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence,with better accuracy,stability and convergence speed.展开更多
文摘This paper describes a method to decompose multi-scale information from different source medical image using wavelet transformation. The data fusion between CT image and MRI image is implemented based on the coefficients fusion rule which included choice of regional variance and weighted average wavelet information. The result indicates that this method is better than WMF, LEF and RVF on fusion results, details and target distortion.
文摘Dempster-Shafer(D-S)evidence theory is a key technology for integrating uncertain information from multiple sources.However,the combination rules can be paradoxical when the evidence seriously conflict with each other.In the paper,we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise(DBSCAN)density clustering.In the proposed mechanism,firstly,the original evidence sets are preprocessed by DBSCAN density clustering,and a successfully focal element similarity criteria is used to mine the potential information between the evidence,and make a correct measure of the conflict evidence.Then,two different discount factors are adopted to revise the original evidence sets,based on the result of DBSCAN density clustering.Finally,we conduct the information fusion for the revised evidence sets by D-S combination rules.Simulation results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence,with better accuracy,stability and convergence speed.
