Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
IEEE Trans Biomed Eng. 2010 Jul;57(7):1707-18. doi: 10.1109/TBME.2010.2042169. Epub 2010 Feb 18.
Detection of vascular bifurcations is a challenging task in multimodal retinal image registration. Existing algorithms based on bifurcations usually fail in correctly aligning poor quality retinal image pairs. To solve this problem, we propose a novel highly distinctive local feature descriptor named partial intensity invariant feature descriptor (PIIFD) and describe a robust automatic retinal image registration framework named Harris-PIIFD. PIIFD is invariant to image rotation, partially invariant to image intensity, affine transformation, and viewpoint/perspective change. Our Harris-PIIFD framework consists of four steps. First, corner points are used as control point candidates instead of bifurcations since corner points are sufficient and uniformly distributed across the image domain. Second, PIIFDs are extracted for all corner points, and a bilateral matching technique is applied to identify corresponding PIIFDs matches between image pairs. Third, incorrect matches are removed and inaccurate matches are refined. Finally, an adaptive transformation is used to register the image pairs. PIIFD is so distinctive that it can be correctly identified even in nonvascular areas. When tested on 168 pairs of multimodal retinal images, the Harris-PIIFD far outperforms existing algorithms in terms of robustness, accuracy, and computational efficiency.
血管分叉的检测是多模态视网膜图像配准中的一项具有挑战性的任务。现有的基于分叉的算法通常无法正确对齐质量较差的视网膜图像对。为了解决这个问题，我们提出了一种新的高度独特的局部特征描述符，名为部分强度不变特征描述符（PIIFD），并描述了一种名为 Harris-PIIFD 的强大的自动视网膜图像配准框架。PIIFD 对图像旋转、图像强度、仿射变换和视点/透视变化具有部分不变性。我们的 Harris-PIIFD 框架由四个步骤组成。首先，由于角点在图像域中是充足且均匀分布的，因此使用角点作为控制点候选，而不是分叉点。其次，为所有角点提取 PIIFD，并应用双边匹配技术来识别图像对之间的对应 PIIFD 匹配。第三，去除错误匹配并细化不准确的匹配。最后，使用自适应变换来注册图像对。PIIFD 非常独特，即使在非血管区域也能正确识别。在 168 对多模态视网膜图像上进行测试时，Harris-PIIFD 在鲁棒性、准确性和计算效率方面均优于现有算法。
