Chanwimaluang Thitiporn, Fan Guoliang, Fransen Stephen R
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater 74078, USA.
IEEE Trans Inf Technol Biomed. 2006 Jan;10(1):129-42. doi: 10.1109/titb.2005.856859.
This work studies retinal image registration in the context of the National Institutes of Health (NIH) Early Treatment Diabetic Retinopathy Study (ETDRS) standard. The ETDRS imaging protocol specifies seven fields of each retina and presents three major challenges for the image registration task. First, small overlaps between adjacent fields lead to inadequate landmark points for feature-based methods. Second, the non-uniform contrast/intensity distributions due to imperfect data acquisition will deteriorate the performance of area-based techniques. Third, high-resolution images contain large homogeneous nonvascular/texureless regions that weaken the capabilities of both feature-based and area-based techniques. In this work, we propose a hybrid retinal image registration approach for ETDRS images that effectively combines both area-based and feature-based methods. Four major steps are involved. First, the vascular tree is extracted by using an efficient local entropy-based thresholding technique. Next, zeroth-order translation is estimated by maximizing mutual information based on the binary image pair (area-based). Then image quality assessment regarding the ETDRS field definition is performed based on the translation model. If the image pair is accepted, higher-order transformations will be involved. Specifically, we use two types of features, landmark points and sampling points, for affine/quadratic model estimation. Three empirical conditions are derived experimentally to control the algorithm progress, so that we can achieve the lowest registration error and the highest success rate. Simulation results on 504 pairs of ETDRS images show the effectiveness and robustness of the proposed algorithm.
本研究在国立卫生研究院（NIH）早期治疗糖尿病性视网膜病变研究（ETDRS）标准的背景下，对视网膜图像配准进行了研究。ETDRS成像协议规定了每个视网膜的七个视野，并给图像配准任务带来了三个主要挑战。第一，相邻视野之间的小重叠导致基于特征的方法的地标点不足。第二，由于数据采集不完善导致的对比度/强度分布不均匀，会降低基于区域的技术的性能。第三，高分辨率图像包含大的均匀无血管/无纹理区域，这削弱了基于特征和基于区域的技术的能力。在这项工作中，我们提出了一种用于ETDRS图像的混合视网膜图像配准方法，该方法有效地结合了基于区域和基于特征的方法。该方法涉及四个主要步骤。首先，使用基于局部熵的高效阈值技术提取血管树。接下来，基于二值图像对（基于区域）通过最大化互信息来估计零阶平移。然后基于平移模型对ETDRS视野定义进行图像质量评估。如果图像对被接受，则将涉及高阶变换。具体来说，我们使用两种类型的特征，即地标点和采样点，来进行仿射/二次模型估计。通过实验得出三个经验条件来控制算法进程，以便我们能够实现最低的配准误差和最高的成功率。对504对ETDRS图像的仿真结果表明了所提算法的有效性和鲁棒性。
