Yang Jinzhu, Deng Meili, Cao Peng, Tan Wenjun, Sun Qi, Lu Lin, Zhao Dazhe
Technol Health Care. 2017 Jul 20;25(S1):251-257. doi: 10.3233/THC-171328.
Differential demon is a fast and efficient registration algorithm. It drives the floating image to deform using the force based on the gradient between the reference and floating image. But it will cause abnormal deformation when the driving force approaches zero,which limits its practical applications.
This paper proposed an improved differential demon algorithm, which aimed to enhance the registration performance of the existing demon algorithm.
Firstly, we review the original differential demon algorithm. Then, we propose an improved differential demon algorithm and the process of mathematical deduction. Finally, we use experiment to prove that the improved differential demon algorithm is effective and it can improve the accuracy of registration.
We tested our method on data sets provided by Xuanwu Hospital Capital Medical University. The registration performance proved to be better than the original demon algorithm in terms of mutual information, normalized correlation coefficient, mean square error and iteration number.
Experiment results demonstrate the superiority of method proposed in this paper to the original demon algorithm.
差分恶魔算法是一种快速高效的配准算法。它利用参考图像和浮动图像之间的梯度力驱动浮动图像变形。但当驱动力接近零时会导致异常变形,这限制了其实际应用。
提出一种改进的差分恶魔算法,旨在提高现有恶魔算法的配准性能。
首先回顾原始差分恶魔算法。然后提出改进的差分恶魔算法及数学推导过程。最后通过实验证明改进的差分恶魔算法有效且能提高配准精度。
在首都医科大学宣武医院提供的数据集上测试了该方法。在互信息、归一化相关系数、均方误差和迭代次数方面,配准性能优于原始恶魔算法。
实验结果证明了本文提出的方法优于原始恶魔算法。