Patient motion artifacts are an important source of data irregularities in OCT imaging. With longer duration OCT scans – as is needed for large wide field of view scans or increased scan density – motion artifacts become increasingly problematic. Strategies to mitigate these motion artifacts are then necessary to ensure OCT data integrity. A popular strategy for reducing motion artifacts in OCT images is to capture two orthogonally oriented volumetric scans containing uncorrelated motion and subsequently reconstructing a motion-free volume by combining information from both datasets. While many different variations of this registration approach have been proposed, even the most recent methods might not be suitable for wide FOV OCT scans which can be lacking in features away from the optic nerve head or arcades. To address this problem, we propose a two-stage motion correction algorithm for wide FOV OCT volumes. In the first step, X and Y axes motion is corrected by registering OCT summed voxel projections (SVPs). To achieve this, we introduce a method based on a custom variation of the dense optical flow technique which is aware of the motion free orientation of the scan. Secondly, a depth (Z axis) correction approach based on the segmentation of the retinal layer boundaries in each B-scan using graph-theory and dynamic programming is applied. This motion correction method was applied to wide field retinal OCT volumes (approximately 80° FOV) of 3 subjects with substantial reduction in motion artifacts.
A novel multiresolution analysis-based stereo matching method using curvelets, support weights, and disparity calibration is proposed. By introducing curvelet decomposition, we obtain the curvelet coefficients in different scales and orientations, and the image points can be better described and represented by these coefficients. By using support weights, the fattening effect suffered by previous methods is reduced. As a result, false matches are reduced greatly and overall accuracy is increased. Disparity calibration smoothes the disparity map and removes the remaining outliers to further improve accuracy. The proposed method is verified and compared extensively with state of the art methods, and good results and improvements are achieved.