Computed tomography(CT) plays an important role in the field of modern medical imaging. Reducing radiation exposure dose without significantly decreasing image's quality is always a crucial issue. Inspired by the ...Computed tomography(CT) plays an important role in the field of modern medical imaging. Reducing radiation exposure dose without significantly decreasing image's quality is always a crucial issue. Inspired by the outstanding performance of total variation(TV) technique in CT image reconstruction, a TV regularization based Bayesian-MAP(MAP-TV) is proposed to reconstruct the case of sparse view projection and limited angle range imaging. This method can suppress the streak artifacts and geometrical deformation while preserving image edges. We used ordered subset(OS) technique to accelerate the reconstruction speed. Numerical results show that MAP-TV is able to reconstruct a phantom with better visual performance and quantitative evaluation than classical FBP,MLEM and quadrate prior to MAP algorithms. The proposed algorithm can be generalized to cone-beam CT image reconstruction.展开更多
Image fusion is an imaging technique to visualize information from multiple imaging sources in one single image,which is widely used in remote sensing,medical imaging etc.In this work,we study two variational approach...Image fusion is an imaging technique to visualize information from multiple imaging sources in one single image,which is widely used in remote sensing,medical imaging etc.In this work,we study two variational approaches to image fusion which are closely related to the standard TV-L_(2) and TV-L_(1) image approximation methods.We investigate their convex optimization formulations,under the perspective of primal and dual,and propose their associated new image decomposition models.In addition,we consider the TV-L_(1) based image fusion approach and study the specified problem of fusing two discrete-constrained images f_(1)(x)∈L_(1) and f_(2)(x)∈L_(2),where L_(1) and L_(2) are the sets of linearly-ordered discrete values.We prove that the TV-L_(1) based image fusion actually gives rise to the exact convex relaxation to the corresponding nonconvex image fusion constrained by the discretevalued set u(x)∈L_(1)∪L_(2).This extends the results for the global optimization of the discrete-constrained TV-L_(1) image approximation[8,36]to the case of image fusion.As a big numerical advantage of the two proposed dual models,we show both of them directly lead to new fast and reliable algorithms,based on modern convex optimization techniques.Experiments with medical images,remote sensing images and multi-focus images visibly show the qualitative differences between the two studied variational models of image fusion.We also apply the new variational approaches to fusing 3D medical images.展开更多
基金National Natural Science Foundation of Chinagrant number:30970866+3 种基金Guangzhou Municipal Science and Technology Projectgrant number:llBppZLjj2120029Guangdong Strategic Emerging Industry Core Technology Researchgrant number:2011A081402003
文摘Computed tomography(CT) plays an important role in the field of modern medical imaging. Reducing radiation exposure dose without significantly decreasing image's quality is always a crucial issue. Inspired by the outstanding performance of total variation(TV) technique in CT image reconstruction, a TV regularization based Bayesian-MAP(MAP-TV) is proposed to reconstruct the case of sparse view projection and limited angle range imaging. This method can suppress the streak artifacts and geometrical deformation while preserving image edges. We used ordered subset(OS) technique to accelerate the reconstruction speed. Numerical results show that MAP-TV is able to reconstruct a phantom with better visual performance and quantitative evaluation than classical FBP,MLEM and quadrate prior to MAP algorithms. The proposed algorithm can be generalized to cone-beam CT image reconstruction.
基金J.Yuan and A.Fenster gratefully acknowledge funding from the Canadian Institutes of Health Research,and the Ontario Institute of Cancer ResearchB.Miles gratefully acknowledges funding from the Graduate Program in BioMedical Engineering at the University of Western Ontario and the Computer Assisted Medical Intervention Training Program,which is funded by the Natural Sciences and Engineer-ing Research Council of Canada.A.Fenster holds a Canada Research Chair in Biomedi-cal Engineering,and acknowledges the support of the Canada Research Chair Program.
文摘Image fusion is an imaging technique to visualize information from multiple imaging sources in one single image,which is widely used in remote sensing,medical imaging etc.In this work,we study two variational approaches to image fusion which are closely related to the standard TV-L_(2) and TV-L_(1) image approximation methods.We investigate their convex optimization formulations,under the perspective of primal and dual,and propose their associated new image decomposition models.In addition,we consider the TV-L_(1) based image fusion approach and study the specified problem of fusing two discrete-constrained images f_(1)(x)∈L_(1) and f_(2)(x)∈L_(2),where L_(1) and L_(2) are the sets of linearly-ordered discrete values.We prove that the TV-L_(1) based image fusion actually gives rise to the exact convex relaxation to the corresponding nonconvex image fusion constrained by the discretevalued set u(x)∈L_(1)∪L_(2).This extends the results for the global optimization of the discrete-constrained TV-L_(1) image approximation[8,36]to the case of image fusion.As a big numerical advantage of the two proposed dual models,we show both of them directly lead to new fast and reliable algorithms,based on modern convex optimization techniques.Experiments with medical images,remote sensing images and multi-focus images visibly show the qualitative differences between the two studied variational models of image fusion.We also apply the new variational approaches to fusing 3D medical images.
