Automated Positioning of Overlapping Eye Fundus Images
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
3-D retinal curvature estimation
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
FABC: retinal vessel segmentation using adaboost
IEEE Transactions on Information Technology in Biomedicine
Robust matching of multi-modal retinal images using radon transform based local descriptor
Proceedings of the 1st ACM International Health Informatics Symposium
Retinal fundus image registration via vascular structure graph matching
Journal of Biomedical Imaging
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Affine camera for 3-d retinal surface reconstruction
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Correlation and local feature based cloud motion estimation
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
Personalized identification of abdominal wall hernia meshes on computed tomography
Computer Methods and Programs in Biomedicine
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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