介绍了一种基于改进SLNC(sum of local normalized correlation,SLNC)的2D-3D医学图像配准方法。首先对CT体积数据进行三线性插值,得到各向分辨率相同的体积数据,采用光线跟踪算法对其进行数字图像重建。针对不同位置和方向的重建图像,...介绍了一种基于改进SLNC(sum of local normalized correlation,SLNC)的2D-3D医学图像配准方法。首先对CT体积数据进行三线性插值,得到各向分辨率相同的体积数据,采用光线跟踪算法对其进行数字图像重建。针对不同位置和方向的重建图像,在灰度级压缩的基础上,用改进SLNC函数评价其与X线透视图像的相似性,利用与Brent相结合的Powell优化方法,搜索出相似性最大时的投影变换参数。将此方法用于移动数字X线投影设备——Biplanar 500采集的X线透视图像与相应CT体积数据的配准实验,得到较好的2D-3D图像配准效果。展开更多
The throughput gain obtained by linear network coding （LNC） grows as the generation size increases, while the decoding complexity also grows exponentially. High decoding complexity makes the decoder to be the bottle...The throughput gain obtained by linear network coding （LNC） grows as the generation size increases, while the decoding complexity also grows exponentially. High decoding complexity makes the decoder to be the bottleneck for high speed and large data transmissions. In order to reduce the decoding complexity of network coding, a segment linear network coding （SLNC） scheme is proposed. SLNC provides a general coding structure for the generation-based network coding. By dividing a generation into several segments and restraining the coding coefficients of the symbols within the same segment, SLNC splits a high-rank matrix inversion into several low-rank matrix inversions, therefore reduces the decoding complexity dramatically. In addition, two coefficient selection strategies are proposed for both centrally controlled networks and distributed networks respectively. The theoretical analysis and simulation results prove that SLNC achieves a fairly low decoding complexity at a cost of rarely few extra transmissions.展开更多
文摘介绍了一种基于改进SLNC(sum of local normalized correlation,SLNC)的2D-3D医学图像配准方法。首先对CT体积数据进行三线性插值,得到各向分辨率相同的体积数据,采用光线跟踪算法对其进行数字图像重建。针对不同位置和方向的重建图像,在灰度级压缩的基础上,用改进SLNC函数评价其与X线透视图像的相似性,利用与Brent相结合的Powell优化方法,搜索出相似性最大时的投影变换参数。将此方法用于移动数字X线投影设备——Biplanar 500采集的X线透视图像与相应CT体积数据的配准实验,得到较好的2D-3D图像配准效果。
基金supported by the National Great Science Specific Project of China (2012ZX03001028)
文摘The throughput gain obtained by linear network coding （LNC） grows as the generation size increases, while the decoding complexity also grows exponentially. High decoding complexity makes the decoder to be the bottleneck for high speed and large data transmissions. In order to reduce the decoding complexity of network coding, a segment linear network coding （SLNC） scheme is proposed. SLNC provides a general coding structure for the generation-based network coding. By dividing a generation into several segments and restraining the coding coefficients of the symbols within the same segment, SLNC splits a high-rank matrix inversion into several low-rank matrix inversions, therefore reduces the decoding complexity dramatically. In addition, two coefficient selection strategies are proposed for both centrally controlled networks and distributed networks respectively. The theoretical analysis and simulation results prove that SLNC achieves a fairly low decoding complexity at a cost of rarely few extra transmissions.
