Multimodal Medical Image Registration Based on Feature Spheres in Geometric Algebra
Multimodal Medical Image Registration Based on Feature Spheres in Geometric Algebra
复制标题
基于几何代数特征球的多模态医学图像配准
DOI：
10.1109/access.2018.2818403
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
He, Zhiquan
中科院分区：
文献类型：
--
作者：
Cao, Wenming;Lyu, Fangfang;He, Zhiquan
医学图像分析中的多模态图像配准是非常具有挑战性的，因为来自不同成像设备的图像中的身体结构的外观可能非常不同。本文提出了一种新的方法[GA加速鲁棒特征（SURF）]，它将几何代数（GA）纳入SURF框架，从图像中检测特征。我们使用共形几何代数（CGA）中制定的特征球的体积数据和注册的多模态医学图像建模。具体来说，我们首先使用GA-SURF从医学图像中提取特征。其次，我们使用特征点构造特征球，并使用CGA在两幅图像中找到特征球的对应关系。这样，我们就可以根据特征球的对应关系来配准图像。RIRE实验结果表明，该方法可以实现多模态图像的高精度配准。最大配准误差小于4 mm。
Multi-modal image registration in medical image analysis is very challenging as the appearance of body structures in images from different imaging devices can be very different. In this paper, we propose a new method [GA-speeded up robust features (SURF)], which incorporates the geometric algebra (GA) into SURF framework, to detect features from images. We model the volumetric data and register the multi-modal medical images using feature spheres formulated in conformal geometric algebra (CGA). Specifically, we first extract features from medical images using GA-SURF. Second, we construct the feature spheres using the feature points and find the correspondence of feature spheres in the two images using CGA. With that, we can register the images based on the correspondence of the feature spheres. The experimental results evaluated by RIRE have shown that our method can register the multi-modal images with high accuracy. The maximum registration error is less than $4mm$ .