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首页> 外文会议>Conference on Medical Imaging 2008: Computer-Aided Diagnosis; 20080219-21; San Diego,CA(US) >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：边缘驱动双引导迭代最近点算法用于多峰视网膜图像配准

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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：通常从眼睛中捕获无红（RF）眼底视网膜图像和荧光素血管造影（FA）序列，以诊断和治疗视网膜异常。借助多模式图像配准，医生可以结合信息来进行准确的手术计划和对疾病进展的定量判断。我们的工作目标是将RF图像与同一只眼睛在同一参考空间中的FA序列对齐。我们的工作受到通用双引导迭代最近点（GDB-ICP）的启发，GDB-ICP是一种使用结构相似性的基于特征的全自动方法。 GDB-ICP等级顺序Lowe关键点匹配，并细化从每个关键点匹配连续计算出的转换。尽管已显示GDB-ICP对具有照明差异的图像对具有鲁棒性，但对于多峰和一些FA组合却表现出非线性照明变化，该性能并不令人满意。我们的算法称为“边缘驱动DBICP”，它通过从梯度幅度图像中提取Lowe关键点，并利用边缘点在全局形状上下文中丰富关键点描述符，来修改关键点匹配的生成以进行初始化。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。共有4985个图像对，其中1323个是多峰对。边缘驱动的DBICP成功注册了所有对中的93％和82％的多峰对，而GDB-ICP分别注册了80％和40％。关于在公共参考空间中整个图像序列的配准，“边缘驱动DBICP”在60个序列中取得了成功，与GDB-ICP相比提高了26％。

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