基于变分和互信息的先验形状配准算法

Prior Shape Registration Algorithm Based on Variation and Mutual Information

针对带先验形状约束的几何活动轮廓模型中的形状配准问题,提出一种基于变分方法和最大互信息准则的先验形状配准算法。利用变分配准模型计算仿射变换参数,将其作为互信息配准算法的初值,通过Powell优化算法计算仿射变换参数的最优解。实验结果表明,该算法在保证配准精度的同时,能明显提高计算效率。

Aiming at the registration problem of Geometric Active Contour（GAC） model with prior shape, this paper proposes a prior shape registration algorithm based on variation method and maximum mutual information criterion. After calculating the affine transform parameters with the variation method, the results are used as the initial values of Powell optimization algorithm to maximize the mutual information between the reference and floating images. Experimental results demonstrate that the proposed algorithm can improve the computational efficiency while maintaining a high registration precision.