Intensity inhomogeneity correction of SD-OCT data using macular flatspace

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.

Keywords: Intensity inhomogeneity correction; Macular flatspace; Optical coherence tomography; Registration; Retina.

Figure 1:

B-scan images acquired on the same subject of (approximately) the same location on two different scanners demonstrating the variability in the intensity profile. The images were acquired on (top) a Heidelberg Spectralis scanner and (bottom) a Zeiss Cirrus scanner.

Figure 2:

A B-scan image in (top) native space and (bottom) MFS.

Figure 3:

A B-scan image in native space and MFS with retinal boundaries overlaid as solid lines in red and regression estimated boundaries overlaid as dashed lines in green.

Figure 4:

The data (a) in MFS is divided by (b) the template image to generate (c) the initial gain field.

Figure 5:

Plots of the mean squared error (MSE) of the gain fields recovered using N3 and N3O while (a) varying and fixing the control point spacing, and (b) varying the control point spacing in the x and y directions while fixing.

Figure 6:

Examples of synthetic OCT data generated from real scans acquired by the (a) Spectralis and (c) Cirrus scanners next to their corresponding real images (b, d).

Figure 7:

En-face plane views of an example (a) Type 1 and (b) Type 2 gain field patterns, each having added variation within each B-scan. In (c) and (d), we show the gain field at the location pointed by the arrow in (b) projected through the volume to cover the whole retina or only the RPE region, respectively.

Figure 8:

Box and whisker plots of (a) the coefficient of variation of the intensities within a select set of layers and (b) the contrast between successive layers. For each box, the median value is indicated by the central black line with the width of the boxes extending to the 25th and 75th percentiles, the whiskers extending to 1.5 times the interquartile range, and single points representing outliers beyond the whiskers.

Figure 9:

Example results from (a) a Spectralis scanner and (b) a Cirrus scanner. For each example, the input image is shown on top, the N3O result in the middle, and the estimated gain field on the bottom. Darker colors in the gain field images indicate smaller gain values.

Figure 10:

Cropped registration results of two separate examples are shown in (a) and (b). Shown are the moving image, the target image, and the registration results both without intensity correction and after running N3O on each image prior to registration. Overlaid segmentation boundaries are shown in the second row of (a) and (b). Manual segmentations are shown for the moving and target images, while the manual labels from the moving image are overlaid after registration on the respective registration images.