一种自适应惯性权重粒子群算法在磁共振图像偏移场矫正中的应用  

Application of an Adaptive Inertia Weight Particle Swarm Algorithm in the Magnetic Resonance Bias Field Correction

作　　者：王昌[1] 秦鑫[1] 刘艳[1] 张文超[1] 

机构地区：[1]新乡医学院生物医学工程学院,新乡453003

出　　处：《生物医学工程学杂志》2016年第3期564-569,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(61305147);新乡医学院科研培育基金资助项目(2014QN142)

摘　　要：为解决用传统粒子群算法估计磁共振(MR)图像偏移场会陷入局部最优的问题,本文提出了一种自适应权重粒子群算法估计MR图像的偏移场。针对传统粒子群算法的缺陷,设计一个衡量早熟收敛程度的指标,根据此指标来自适应地调整惯性权重,确保粒子群有效地进行全局寻优,避免陷入局部最优。本文利用Legendre多项式来拟合偏移场,多项式参数利用本文提出的算法进行寻优,最后对MR图像的偏移场进行估计和矫正。将本文算法与改进的熵最小方法进行对比分析,本文矫正后图像熵值更小,对偏移场估计更准确,将矫正后的图像进行分割,分割精度提高将近10%。研究结果初步说明,本算法可应用于MR图像偏移场的矫正。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 es timated 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.

关 键 词：磁共振图像 偏移场 图像熵 自适应 粒子群算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R445.2[自动化与计算机技术—控制科学与工程]