Since the problems of branch loss and fracture in retinal blood vessel segmentation algorithms,an image segmentation method is proposed based on improved pulse coupled neural network(PCNN)and gray wolf optimization al...Since the problems of branch loss and fracture in retinal blood vessel segmentation algorithms,an image segmentation method is proposed based on improved pulse coupled neural network(PCNN)and gray wolf optimization algorithm(GWO).Simplifying the neuron input domain and neuron connection domain of the PCNN network,increasing the gradient information factor in the internal activity items,reducing the model parameters,enhancing the pulse issuing ability,and the optimal parameters of the network are automatically obtained based on multiple feature evaluation criteria and the GWO algorithm.The test in the public data set drive shows that the sensitivity,accuracy,precision,and specificity of the algorithm are 0.799549,0.962789,0.889163,and 0.986552,respectively.The accuracy and specificity are better than the classical segmentation algorithm.It solved the influence of low illumination,optic disc highlight,and foveal shadow on vascular segmentation,and showed excellent performance of vessel connectivity and terminal sensitivity.展开更多
Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical ...Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.展开更多
基金The 2019 Guangdong Province General College Youth Innovation Talent Project(2019GKQNCX009)。
文摘Since the problems of branch loss and fracture in retinal blood vessel segmentation algorithms,an image segmentation method is proposed based on improved pulse coupled neural network(PCNN)and gray wolf optimization algorithm(GWO).Simplifying the neuron input domain and neuron connection domain of the PCNN network,increasing the gradient information factor in the internal activity items,reducing the model parameters,enhancing the pulse issuing ability,and the optimal parameters of the network are automatically obtained based on multiple feature evaluation criteria and the GWO algorithm.The test in the public data set drive shows that the sensitivity,accuracy,precision,and specificity of the algorithm are 0.799549,0.962789,0.889163,and 0.986552,respectively.The accuracy and specificity are better than the classical segmentation algorithm.It solved the influence of low illumination,optic disc highlight,and foveal shadow on vascular segmentation,and showed excellent performance of vessel connectivity and terminal sensitivity.
基金funded by the National Natural Science Foundation of China,grant number 61302188.
文摘Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.
