Wang Chang, Qin Xin, Liu Yan, Zhang Wenchao
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2016 Jun;33(3):564-9.
An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance(MR)image bias field.An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm.The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum.The Legendre polynomial was used to fit bias field,the polynomial parameters were optimized globally,and finally the bias field was estimated and corrected.Compared to those with the improved entropy minimum algorithm,the entropy of corrected image was smaller and the estimated bias field was more accurate in this study.Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm.This algorithm can be applied to the correction of MR image bias field.
本研究提出一种自适应惯性权重粒子群算法，以解决传统粒子群优化方法在估计磁共振（MR）图像偏置场过程中的局部最优问题。针对传统粒子群优化算法的缺陷，设计了一个衡量早熟收敛程度的指标。基于该指标对惯性权重进行自适应调整，以确保粒子群能够进行全局优化，避免陷入局部最优。采用勒让德多项式拟合偏置场，对多项式参数进行全局优化，最终估计并校正偏置场。与改进的熵最小化算法相比，本研究中校正后图像的熵更小，估计的偏置场更准确。然后对校正后的图像进行分割，本研究获得的分割精度比改进的熵最小化算法高10%。该算法可应用于MR图像偏置场的校正。
