A neural network approach to real-time discrete tomography

被引:0
作者
Batenburg, K. J. [1 ]
Kostersi, W. A.
机构
[1] Leiden Univ, Leiden, Netherlands
[2] CWI, NL-1009 AB Amsterdam, Netherlands
来源
COMBINATORIAL IMAGE ANALYSIS, PROCEEDINGS | 2006年 / 4040卷
关键词
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Tomography deals with the reconstruction of the density distribution inside an unknown object from its projections in several directions. In Discrete tomography one focuses on the reconstruction of objects having a small, discrete set of density values. Using this prior knowledge in the reconstruction algorithm may vastly reduce the number of projections that is required to obtain high quality reconstructions. Recently the first generation of real-time tomographic scanners has appeared, capable of acquiring several images per second. Discrete tomography is well suited for real-time operation, as only few projections are required, reducing scanning time. However, for efficient real-time operation an extremely fast reconstruction algorithm is also required. In this paper we present a new reconstruction method, which is based on a feed-forward neural network. The network can compute reconstructions extremely fast, making it suitable for real-time tomography. Our experimental results demonstrate that the approach achieves good reconstruction quality.
引用
收藏
页码:389 / 403
页数:15
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