摘要
传统的图像重建算法存在光源分布不均以及噪声干扰等问题,导致图像重建效果差。针对该问题,提出了一种改进的混合图割算法和梯度算法的发光体图像重建技术。算法首先采用图像分割算法得到在未知先验条件的情况下的发光源情况;然后利用不同的梯度算法,根据重建状态得到发光源准确的分布情况;最后利用内部光源的多级网络提高计算速度和重建的准确性。仿真实验结果表明,本方法即使在存在检测噪声和模型结构误差的情况下,仍然能够得到很好的重建性能,具有较高的实际应用价值。
Image reconstruction is a promising optical molecular imaging technique on the frontier of biomedical optics.In this paper,a generalized hybrid algorithm for image reconstruction was proposed based on graph cut algorithm and gradient-based algorithms.The graph cut algorithm is adopted to estimate a reliable source support without prior knowledge,and different gradient-based algorithms are sequentially used to acquire an accurate and fine source distribution according to the reconstruction status.Furthermore,multilevel meshes for the internal sources are used to speed up the computation and improve the accuracy of reconstruction.Numerical simulations were performed to validate this proposed algorithm and demonstrate its high performance in the multi-source situation even if the detection noises,optical property errors and phantom structure errors are involved in the forward imaging.
出处
《计算机科学》
CSCD
北大核心
2014年第4期314-318,共5页
Computer Science
基金
国家自然科学基金课题(11JJ6056)资助
关键词
图像重建
图像分割
梯度算法
模型结构误差
Image reconstruction
Graph cuts
Gradient-based algorithm
Phantom structure error
