Visual Computing and Image Processing Lab
Oklahoma State University

Imaging, Processing, Inferencing and Learning



Retinal Image Registration

We have studied retinal image registration in the context of the National Institutes of Health (NIH), Early Treatment Diabetic Retinopathy Study (ETDRS) standard protocol. The ETDRS protocol defines seven 30-degree fields of each retina with specific field coverage. A robust ETDRS image registration algorithm is required to (1) assess image quality in terms of ETDRS field coverage, and to (2) support ETDRS-based disease staging. Three major challenges are present. First, small overlaps between adjacent fields lead to inadequate landmark points (crossovers and bifurcations) for feature-based methods. Second, the contrast and intensity distributions within an image are not spatially uniform or consistent. This can deteriorate the performance of area-based techniques. Third, high-resolution ETDRS images contain large homogeneous nonvascular/textureless regions which result in difficulties for both feature-based and area-based techniques. In this work, we propose an ETDRS image registration algorithm which effectively combines both area-based and feature-based methods

Retinal image registration example.

Related Publications

  • T. Chanwimaluang, G. Fan, and S. Fransen, “Hybrid Retinal Image Registration”, IEEE Trans. on Information Technology in Biomedicine, Vol. 10, No. 1, , pp129-142, Jan. 2006.

  • T. Chanwimaluang and G. Fan, “Retinal Image Registration for NIH’s ETDRS", in Proc. International Symposium on Visual Computing, Lake Tahoe, Nevada, Dec. 2005, also in LNCS, Vol. 3804, Editors: G. Bebis, Springer, 2005.

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