Abstract:

Optical Coherence Tomography Angiography (OCTA) is a promising imaging tool for disease diagnosis. Many studies have used quantification methods for objective assessment of the OCTA data. However, investigations into the loss of information in OCTA images due to the segmentation of vessels have not been explored. This thesis will, therefore, use Differential Box Counting (DBC), a texture-based quantification tool that does not require segmentation, to assess the loss of information due to segmentation. This thesis investigated the differences in P-values and ICC values between three different fractal dimension measurements: box counting dimension, information dimension, and differential box counting. Box counting and information dimension measurements require a segmentation prepossessing step for calculation. The data collected includes OCTA images of 9 valid eyes in two time points that were short enough so that no expected changes to the vasculature are observed. All fractal dimension methods, including the non-segmentation- based result were found to be statistically significant, although the non-segmentation-based algorithms were found to have higher ICC values. DBC is thought to be more sensitive to changes in the OCTA images when compared to the segmentation-based algorithms. Thus, variances in the OCTA images as well as artefacts in the OCTA image affect the non-segmentation-based measurements more than the segmentation-based measurements.