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Registration of the Cone Beam CT and Blue-Ray Scanned Dental Model Based on the Improved ICP Algorithm

机译:基于改进ICP算法的锥束CT和蓝光牙科模型的配准

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摘要

Multimodality image registration and fusion has complementary significance for guiding dental implant surgery. As the needs of the different resolution image registration, we develop an improved Iterative Closest Point (ICP) algorithm that focuses on the registration of Cone Beam Computed Tomography (CT) image and high-resolution Blue-light scanner image. The proposed algorithm includes two major phases, coarse and precise registration. Firstly, for reducing the matching interference of human subjective factors, we extract feature points based on curvature characteristics and use the improved three point's translational transformation method to realize coarse registration. Then, the feature point set and reference point set, obtained by the initial registered transformation, are processed in the precise registration step. Even with the unsatisfactory initial values, this two steps registration method can guarantee the global convergence and the convergence precision. Experimental results demonstrate that the method has successfully realized the registration of the Cone Beam CT dental model and the blue-ray scanner model with higher accuracy. So the method could provide researching foundation for the relevant software development in terms of the registration of multi-modality medical data.
机译:多模态图像配准和融合对于指导种植牙手术具有互补的意义。根据不同分辨率图像配准的需要,我们开发了一种改进的迭代最近点(ICP)算法,该算法着重于锥束计算机断层扫描(CT)图像和高分辨率蓝光扫描仪图像的配准。所提出的算法包括两个主要阶段,粗糙和精确配准。首先,为了减少人为主观因素的匹配干扰,我们基于曲率特征提取特征点,并使用改进的三点平移变换方法实现粗配准。然后,在精确配准步骤中处理通过初始配准变换获得的特征点集和参考点集。即使初始值不令人满意,这两个步骤的配准方法也可以保证全局收敛性和收敛精度。实验结果表明,该方法已经成功地实现了锥束CT牙科模型和蓝光扫描仪模型的配准,且精度更高。因此,该方法可以为多模态医学数据的注册提供相关软件开发的研究基础。

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