Patankar Sanika S, Kulkarni Jayant V
Vishwakarma Institute of Technology, Pune, India.
Comput Methods Programs Biomed. 2015 May;119(3):121-41. doi: 10.1016/j.cmpb.2015.02.009. Epub 2015 Mar 16.
Retinal image registration is a necessary step in diagnosis and monitoring of Diabetes Retinopathy (DR), which is one of the leading causes of blindness. Long term diabetes affects the retinal blood vessels and capillaries eventually causing blindness. This progressive damage to retina and subsequent blindness can be prevented by periodic retinal screening. The extent of damage caused by DR can be assessed by comparing retinal images captured during periodic retinal screenings. During image acquisition at the time of periodic screenings translation, rotation and scale (TRS) are introduced in the retinal images. Therefore retinal image registration is an essential step in automated system for screening, diagnosis, treatment and evaluation of DR. This paper presents an algorithm for registration of retinal images using orthogonal moment invariants as features for determining the correspondence between the dominant points (vessel bifurcations) in the reference and test retinal images. As orthogonal moments are invariant to TRS; moment invariants features around a vessel bifurcation are unaltered due to TRS and can be used to determine the correspondence between reference and test retinal images. The vessel bifurcation points are located in segmented, thinned (mono pixel vessel width) retinal images and labeled in corresponding grayscale retinal images. The correspondence between vessel bifurcations in reference and test retinal image is established based on moment invariants features. Further the TRS in test retinal image with respect to reference retinal image is estimated using similarity transformation. The test retinal image is aligned with reference retinal image using the estimated registration parameters. The accuracy of registration is evaluated in terms of mean error and standard deviation of the labeled vessel bifurcation points in the aligned images. The experimentation is carried out on DRIVE database, STARE database, VARIA database and database provided by local government hospital in Pune, India. The experimental results exhibit effectiveness of the proposed algorithm for registration of retinal images.
视网膜图像配准是糖尿病视网膜病变（DR）诊断和监测中的必要步骤，糖尿病视网膜病变是导致失明的主要原因之一。长期糖尿病会影响视网膜血管和毛细血管，最终导致失明。通过定期视网膜筛查可以预防视网膜的这种渐进性损伤及其导致的失明。通过比较定期视网膜筛查期间拍摄的视网膜图像，可以评估糖尿病视网膜病变造成的损伤程度。在定期筛查时进行图像采集时，视网膜图像中会引入平移、旋转和缩放（TRS）。因此，视网膜图像配准是糖尿病视网膜病变筛查、诊断、治疗和评估自动化系统中的关键步骤。本文提出了一种基于正交矩不变量的视网膜图像配准算法，该算法将正交矩不变量作为特征来确定参考视网膜图像和测试视网膜图像中关键点（血管分叉点）之间的对应关系。由于正交矩对TRS具有不变性，血管分叉点周围的矩不变量特征不会因TRS而改变，可用于确定参考视网膜图像和测试视网膜图像之间的对应关系。血管分叉点位于分割、细化（单像素血管宽度）的视网膜图像中，并在相应的灰度视网膜图像中进行标记。基于矩不变量特征建立参考视网膜图像和测试视网膜图像中血管分叉点之间的对应关系。此外，使用相似变换估计测试视网膜图像相对于参考视网膜图像的TRS。利用估计的配准参数将测试视网膜图像与参考视网膜图像对齐。根据对齐图像中标记血管分叉点的平均误差和标准差来评估配准的准确性。实验在DRIVE数据库、STARE数据库、VARIA数据库以及印度浦那当地政府医院提供的数据库上进行。实验结果表明了所提算法在视网膜图像配准方面的有效性。
