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首页> 美国卫生研究院文献>other >A Robust and Accurate Two-Step Auto-Labeling Conditional Iterative Closest Points (TACICP) Algorithm for Three-Dimensional Multi-Modal Carotid Image Registration
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A Robust and Accurate Two-Step Auto-Labeling Conditional Iterative Closest Points (TACICP) Algorithm for Three-Dimensional Multi-Modal Carotid Image Registration

机译：三维多模态颈动脉图像配准的鲁棒且精确的两步自动标签条件迭代最近点（TACICP）算法

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Atherosclerosis is among the leading causes of death and disability. Combining information from multi-modal vascular images is an effective and efficient way to diagnose and monitor atherosclerosis, in which image registration is a key technique. In this paper a feature-based registration algorithm, Two-step Auto-labeling Conditional Iterative Closed Points (TACICP) algorithm, is proposed to align three-dimensional carotid image datasets from ultrasound (US) and magnetic resonance (MR). Based on 2D segmented contours, a coarse-to-fine strategy is employed with two steps: rigid initialization step and non-rigid refinement step. Conditional Iterative Closest Points (CICP) algorithm is given in rigid initialization step to obtain the robust rigid transformation and label configurations. Then the labels and CICP algorithm with non-rigid thin-plate-spline (TPS) transformation model is introduced to solve non-rigid carotid deformation between different body positions. The results demonstrate that proposed TACICP algorithm has achieved an average registration error of less than 0.2mm with no failure case, which is superior to the state-of-the-art feature-based methods.
机译：动脉粥样硬化是死亡和残疾的主要原因。结合来自多模态血管图像的信息是诊断和监测动脉粥样硬化的有效途径，其中图像配准是一项关键技术。本文提出了一种基于特征的配准算法，即两步自动标记条件迭代闭合点（TACICP）算法，以对齐来自超声（US）和磁共振（MR）的三维颈动脉图像数据集。基于2D分割轮廓，采用了从粗到精的策略，包括两个步骤：刚性初始化步骤和非刚性细化步骤。在刚性初始化步骤中给出了条件迭代最近点（CICP）算法，以获得鲁棒的刚性变换和标签配置。然后引入标签和具有非刚性薄板样条（TPS）转换模型的CICP算法，以解决不同体位之间颈动脉的非刚性变形。结果表明，所提出的TACICP算法在没有故障情况下实现了小于0.2mm的平均配准误差，这优于最新的基于特征的方法。

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