Published In
Medical Image Analysis
Document Type
Pre-Print
Publication Date
1-1-2018
Subjects
Optical coherence tomography, Retina -- Imaging, Image registration, Image processing -- Digital techniques
Abstract
Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images.
DOI
10.1016/j.media.2017.09.008
Persistent Identifier
http://archives.pdx.edu/ds/psu/22092
Citation Details
Lang, Andrew; Carass, Aaron; Jedynak, Bruno M.; Solomon, Sharon D.; Calabresi, Peter A.; and Prince, Jerry L., "Intensity Inhomogeneity Correction of SD-OCT Data Using Macular Flatspace" (2018). Mathematics and Statistics Faculty Publications and Presentations. 196.
http://archives.pdx.edu/ds/psu/22092
Description
NOTICE: this is the author’s preprint version of a work that was accepted for publication in Medical Image Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Medical Image Analysis, 43, (October 2017). https://doi.org/10.1016/j.media.2017.09.008