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首页> 外文期刊>Medical Imaging, IEEE Transactions on >The Edge-Driven Dual-Bootstrap Iterative Closest Point Algorithm for Registration of Multimodal Fluorescein Angiogram Sequence
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The Edge-Driven Dual-Bootstrap Iterative Closest Point Algorithm for Registration of Multimodal Fluorescein Angiogram Sequence

机译：边缘驱动双引导迭代最近点算法注册多峰荧光素血管造影序列

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摘要

Motivated by the need for multimodal image registration in ophthalmology, this paper introduces an algorithm which is tailored to jointly align in a common reference space all the images in a complete fluorescein angiogram (FA) sequence, which contains both red-free (RF) and FA images. Our work is inspired by Generalized Dual-Bootstrap Iterative Closest Point (GDB-ICP), which rank-orders Lowe keypoint matches and refines the transformation, going from local and low-order to global and higher-order model, computed from each keypoint match in succession. Albeit GDB-ICP has been shown to be robust in registering images taken under different lighting conditions, the performance is not satisfactory for image pairs with substantial, nonlinear intensity differences. Our algorithm, named Edge-Driven DB-ICP, targeting the least reliable component of GDB-ICP, modifies generation of keypoint matches for initialization by extracting the Lowe keypoints from the gradient magnitude image and enriching the keypoint descriptor with global-shape context using the edge points. Our dataset consists of 60 randomly-selected pathological sequences, each on average having up to two RF and 13 FA images. Edge-Driven DB-ICP successfully registered 92.4% of all pairs, and 81.1% multimodal pairs, whereas GDB-ICP registered 80.1% and 40.1%, respectively. For the joint registration of all images in a sequence, Edge-Driven DB-ICP succeeded in 59 sequences, which is a 23% improvement over GDB-ICP.
机译：出于眼科对多模态图像配准的需求，本文引入了一种算法，该算法旨在将一个完整的荧光素血管造影（FA）序列中的所有图像联合排列在一个公共参考空间中，该序列包含无红光（RF）和FA图像。我们的工作受到通用双引导迭代最近点（GDB-ICP）的启发，广义双引导迭代最近点（GDB-ICP）对Lowe关键点匹配进行排序，并细化从本地和低阶模型到全局和更高阶模型的转换，并根据每个关键点匹配计算得出陆续。尽管已显示GDB-ICP在配准在不同光照条件下拍摄的图像方面具有鲁棒性，但对于具有明显的非线性强度差的图像对来说，性能并不令人满意。我们的算法名为Edge-Driven DB-ICP，其针对GDB-ICP的最不可靠的组件，它通过从梯度幅度图像中提取Lowe关键点并使用全局形状上下文丰富了关键点描述符，从而修改了关键点匹配的生成以进行初始化。边缘点。我们的数据集由60个随机选择的病理序列组成，每个序列平均最多具有两个RF和13个FA图像。边缘驱动的DB-ICP成功注册了所有对中的92.4％和多模态对中的81.1％，而GDB-ICP分别注册了80.1％和40.1％。对于序列中所有图像的联合配准，Edge-Driven DB-ICP成功实现了59个序列，比GDB-ICP改进了23％。

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