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首页> 外文会议>Conference on Medical Imaging: Computer-Aided Diagnosis >The Edge-Driven Dual-Bootstrap Iterative Closest Point Algorithm for Multimodal Retinal Image Registration
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The Edge-Driven Dual-Bootstrap Iterative Closest Point Algorithm for Multimodal Retinal Image Registration

机译：多模式视网膜图像配准的边缘驱动双引导迭代接近点算法

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Red-free (RF) fundus retinal images and fluorescein angiogram (FA) sequence are often captured from an eye for diagnosis and treatment of abnormalities of the retina. With the aid of multimodal image registration, physicians can combine information to make accurate surgical planning and quantitative judgment of the progression of a disease. The goal of our work is to jointly align the RF images with the FA sequence of the same eye in a common reference space. Our work is inspired by Generalized Dual-Bootstrap Iterative Closest Point (GDB-ICP), which is a fully-automatic, feature-based method using structural similarity. GDB-ICP rank-orders Lowe keypoint matches and refines the transformation computed from each keypoint match in succession. Albeit GDB-ICP has been shown robust to image pairs with illumination difference, the performance is not satisfactory for multimodal and some FA pairs which exhibit substantial non-linear illumination changes. Our algorithm, named Edge-Driven DBICP, 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 61 randomly selected pathological sequences, each on average having two RF and 13 FA images. There are total of 4985 image pairs, out of which 1323 are multimodal pairs. Edge-Driven DBICP successfully registered 93% of all pairs, and 82% multimodal pairs, whereas GDB-ICP registered 80% and 40%, respectively. Regarding registration of the whole image sequence in a common reference space, Edge-Driven DBICP succeeded in 60 sequences, which is 26% improvement over GDB-ICP.
机译：无红（RF）眼底视网膜图像和荧光素血管造影（FA）序列通常被捕获用于诊断和治疗视网膜的异常。借助多式联运图像配准，医生可以结合信息以进行准确的手术规划和对疾病进展的定量判断。我们的作品的目标是将RF图像与共同参考空间中同一眼睛的FA序列共同对齐。我们的工作受到广义双引导迭代点（GDB-ICP）的启发，它是一种使用结构相似性的全自动功能的方法。 GDB-ICP等级命令Lowe Keypoint匹配并在继承中从每个Keypoint匹配中匹配的转换。虽然GDB-ICP已被证明对具有照明差异的图像对具有稳健性，但对于多模式和一些FA对具有令人满意的性能，其表现出实质性的非线性照明变化。我们的算法，命名为Edge驱动的DBICP，通过从梯度幅度图像中提取Lowe键点来修改初始化的Keypoint匹配，并使用边缘点从全局形状上下文丰富关键点描述符。我们的数据集由61个随机选择的病理序列组成，每个序列平均具有两个RF和13个FA图像。总共有4985个图像对，其中1323是多模式对。边缘驱动的DBICP成功注册了所有对的93％，82％的多模态对，而GDB-ICP分别注册了80％和40％。关于在公共参考空间中的整个图像序列的登记，边缘驱动的DBICP成功成功60个序列，这与GDB-ICP的改进是26％。

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