【摘要】 Demons算法是一种基于光流场模型的小形变非刚性配准算法,大形变情况下不具有拓扑保持性,将它用于颅脑CT图像配准时效果不理想。为此,本研究对它进行了改进。首先建立Demons算法目标能量函数,将形变场求解转化为目标函数优化问题;然后通过增加sKL距离作为正则项来优化目标函数,消除了形变场的不适定性,并使形变场更加光滑。对高分辨率颅脑CT图像的实验结果表明,改进算法不仅能够处理大形变问题,还能在处理大形变时通过光滑的形变场得到更精确的配准结果。更多还原

【Abstract】 Demons is a non-rigid image registration algorithm which is derived by assuming small deformations.One of the limitations of the original Demons is that it can not produce topology preserving maps for the large deformations.Aiming to solve this problem,an improved Demons algorithm was proposed in this paper.First,the equation of force in the original Demons was regarded as the result of minimizing the energy function.Then,Demons algorithm was improved by adding a regularization term into the function.The symmetric Kullback-Leibler(sKL) distance in information theory was used as the regularization term.The experiment results with high resolution CT cerebral images demonstrated that the improved algorithm could not only handle large deformations,but also obtain more accurate registration results using smooth deformation fields.更多还原