移动队列规则耦合角度约束的医学图像匹配

发布时间：2018-04-26 23:04

  本文选题：医学图像匹配 + 移动队列规则　； 参考：《西南大学学报(自然科学版)》2017年12期

【摘要】：当前医学图像的特征匹配主要依靠像素灰度来完成,但是像素灰度对空间信息不敏感,当匹配图像之间存在灰度信息不均衡以及噪声干扰时,将导致误匹配率较高,对此,本文提出了一种基于移动队列规则耦合角度约束的医学图像匹配算法.首先,利用高斯金字塔模型对源图像进行滤波预处理,以减少源图像中存在的噪声等干扰;再利用Harris算子对预处理后的源图像进行特征检测,获取图像的特征点;然后,利用SURF(Speed Up Robust Feature)特征描述子,获取特征点对应的特征描述子.并通过尺度空间理论获取特征点集,通过将特征点集进行排序来形成队列,从而设计移动队列规则,完成特征点的匹配;最后,通过求取匹配特征点间的夹角,形成角度约束模型,对匹配特征点进行提纯,剔除伪匹配特征点,使得匹配准确度得以提升.从仿真实验结果与分析可见,在对医学图像进行匹配时,本文所提出的方法具有匹配精度高、鲁棒性能好等特点.
[Abstract]:At present, the feature matching of medical image mainly depends on pixel gray level, but pixel gray level is not sensitive to spatial information. When there is imbalance of gray level information and noise interference between matching images, the mismatch rate will be high. In this paper, a medical image matching algorithm based on the coupling angle constraint of mobile queue rules is proposed. Firstly, using Gao Si pyramid model to filter and preprocess the source image to reduce the noise and other interference in the source image, then using the Harris operator to detect the feature of the preprocessed source image and obtain the feature points of the image. The feature descriptor corresponding to feature points is obtained by using SURF(Speed up Robust feature descriptor. And through the theory of scale space to obtain the feature points set, by sorting the feature points set to form a queue, so as to design the mobile queue rules, complete the matching of feature points; finally, through the calculation of the matching angle between feature points, The angle constraint model is formed, the matching feature points are purified, and the pseudo matching feature points are eliminated, so the matching accuracy can be improved. From the simulation results and analysis, it can be seen that the method proposed in this paper has the characteristics of high matching accuracy and good robustness when matching medical images.
【作者单位】： 江苏医药职业学院图书信息中心;
【基金】：江苏省自然科学基金项目(BK2015609)
【分类号】：R318;TP391.41
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本文编号：1808063