In the process of display, manipulation and analysis of biomedical image data, they usually need to be converted to data of isotropic discretization through the process of interpolation, while the cubic convolution in...In the process of display, manipulation and analysis of biomedical image data, they usually need to be converted to data of isotropic discretization through the process of interpolation, while the cubic convolution interpolation is widely used due to its good tradeoff between computational cost and accuracy. In this paper, we present a whole concept for the 3D medical image interpolation based on cubic convolution, and the six methods, with the different sharp control parameter,which are formulated in details. Furthermore, we also give an objective comparison for these methods using data sets with the different slice spacing. Each slice in these data sets is estimated by each interpolation method and compared with the original slice using three measures: mean-squared difference, number of sites of disagreement, and largest difference. According to the experimental results, we present a recommendation for 3D medical images under the different situations in the end.展开更多
Image interpolation of cross-sections is one of the key steps of medical visualization, and the cubic convolution interpolation is usually employed due to its good tradeoff between computational cost and accuracy, how...Image interpolation of cross-sections is one of the key steps of medical visualization, and the cubic convolution interpolation is usually employed due to its good tradeoff between computational cost and accuracy, however, sometimes its accuracy can still not meet the requirement. Aimed at the problem, in this paper, the interpolation principle based cubic convolution is firstly analyzed systematically, and then essential relationship among the different cubic convolution interpolation methods is clarified. Lastly, a novel cross-section interpolation method for medical images that is based on the optimal parameter of sharp control is presented. The method takes full advantage of the local characteristic of medical images, and the optimized sharp control parameter is obtained by the iterative computation, and then the cross-section interpolation is performed by the cubic convolution with the optimized parameter in one time.The experimental results show that the method presented in the paper not only can improve the interpolation accuracy effectively, but also is robust.展开更多
文摘In the process of display, manipulation and analysis of biomedical image data, they usually need to be converted to data of isotropic discretization through the process of interpolation, while the cubic convolution interpolation is widely used due to its good tradeoff between computational cost and accuracy. In this paper, we present a whole concept for the 3D medical image interpolation based on cubic convolution, and the six methods, with the different sharp control parameter,which are formulated in details. Furthermore, we also give an objective comparison for these methods using data sets with the different slice spacing. Each slice in these data sets is estimated by each interpolation method and compared with the original slice using three measures: mean-squared difference, number of sites of disagreement, and largest difference. According to the experimental results, we present a recommendation for 3D medical images under the different situations in the end.
文摘Image interpolation of cross-sections is one of the key steps of medical visualization, and the cubic convolution interpolation is usually employed due to its good tradeoff between computational cost and accuracy, however, sometimes its accuracy can still not meet the requirement. Aimed at the problem, in this paper, the interpolation principle based cubic convolution is firstly analyzed systematically, and then essential relationship among the different cubic convolution interpolation methods is clarified. Lastly, a novel cross-section interpolation method for medical images that is based on the optimal parameter of sharp control is presented. The method takes full advantage of the local characteristic of medical images, and the optimized sharp control parameter is obtained by the iterative computation, and then the cross-section interpolation is performed by the cubic convolution with the optimized parameter in one time.The experimental results show that the method presented in the paper not only can improve the interpolation accuracy effectively, but also is robust.
