摘要
针对传统的基于几何度量的配准方法无法配准存在局部变形的医学器官的问题,提出了应用显著纹理特征的经典迭代最近点(ICP)医学图像配准算法。该方法借鉴主动外观模型(AAM)思想对医学图像的显著纹理特征建模,将显著性强的特征点赋予较大权重,率先配准。在传统基于空间距离的图像配准基础上加入显著纹理距离。然后,模拟格式塔心理学提出的人类视觉认知过程,使用线性递减的权重平衡两种"距离"度量方式。该算法前期主要根据几何距离取得整体配准效果,后期依赖图像纹理特征使存在局部变形位置的特征点也能精确配准。最后,在腹腔肝脏图像上进行实验。实验结果表明该算法取得了较好的配准效果,准确率达78.82%,比其他几种流行算法提高了22.22%,且对图像的旋转变化不敏感。提出的算法基本解决了存在局部变形医学器官图像的配准问题,达到了精度高、鲁棒性强的配准效果。
Traditional registration methods based on geometric measurement can not match the medical image with local deformation. To solve the problem, an improved Iterative Closest Points （ICP） algorithm about human visual cognitive process is proposed base on the salient texture. Firstly, the method establishes the model for the salient texture feature of a medical image based on Active Appearance Model（AAM ）algorithm, and it gives the feature point with more salient for a larger weight to complete the image match in the first step. Then,it introduces the salient texture distance to the traditional space distance. By simulating the human visual cognitive process proposed by Gestalt, the linear decreasing weight is used to balance the two kinds of distance measuring methods. With the algorithm, a whole registration is obtained by the geometric distance in the early stage. On the other hand, the feature points of local deformation are accurately registrated with the texture features in the later stage. At last, a series of experiments are performed on real live images. The experiment results show that the algorithm can get a good matching result, and the registration accuracy is 78.82%, increasing by 22.22 % as compared with those of other popular algorithms. The experimental results also show that it is not sensitive to the rotation of the images. It concludes that the algorithm solves the registration problems of local deformation in medical organs and achieves higher precision and stronger robustness.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2015年第9期2656-2665,共10页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.61372046)
陕西省自然科学基金资助项目(No.2014JM8338)
陕西省教育厅科学研究计划资助项目(No.11JK1049)
关键词
显著纹理
医学图像
图像配准
迭代最近点算法(ICP)
视觉认知
salient texture
medical image
image registration
Iterative Closest Point （ICP）algorithm
vision cognition
