Automated segmentation of ophthalmological OCT images

Abstract

OCT (Optical coherence tomography) has become a popular method for macular degeneration diagnosis. The advantages over other methods are: OCT is noninvasive, it has a high penetration and it has a high resolution. However, the always present speckle noise and the low contrast differences make it hard to segment the layers for the measurements correctly. The aim of this paper is to show the importance of optimizing the retinal segmentation process. Actual automatic segmentation algorithms are capable of detecting up to eleven layers in real time, but often fail at images with (strong) macular degeneration, which are complicating the separation of the layers from each other. This paper sums up some actual aspects of developments in retinal segmentation and shows the limits of actual algorithms. As a comprehensive test process for this paper, we tested all common image processing algorithms and implemented found promising, modern OCT segmentation methods. The result is a wide scale analysis which can be used as a roadmap for optimizing the process of retinal segmentation. Promising algorithms were found with the Canny edge detector, graph cuts and dynamic programming. Combining these algorithms results, the graph-, gradient-, intensity information, and decreasing the search region step by step has shown to be a fast and reliable solution. All tests were using 2D image data, 3D data could be used as well but plays no role in this paper. The testing process includes pre-filtering for image denoising, which can be done fast and is creating better preconditions for the segmentation process.