Automated Image Threshold Method Comparison for Conjunctival Vessel Quantification on Optical Coherence Tomography Angiography

Abstract

Purpose To determine the impact of image binarization and the best thresholding method for conjunctival optical coherence tomography angiography (OCTA). Methods Vessel density (VD) of 14 OCTA conjunctival images (nine nasal and five temporal conjunctivas, and eight right and six left eyes) from normal subjects was analyzed. The binarization of gold-standard images, created by removing pixels that do not represent vessels on ImageJ software, was assessed by three masked graders to determine consistency of VD for images. Various thresholding methods on ImageJ, including manual, 1-, 2- and 3-step processes, were performed on unprocessed images for comparison. Interclass correlation coefficient (ICC) ≥0.750 were classified as good reliability and selected for calculation of the performance of the pixel location in the binarized images of each method. Results Analysis of the gold-standard threshold method achieved an ICC of 0.816 with excellent agreement ( R 2 = 0.965, P < 0.001). From a total 28 different methods and variations performed, only nine methods performed with good reliability, including two 1-step thresholds, six 2-step thresholds, and one 3-step threshold method. Overall, 2-step threshold methods were more reliable than 3-step threshold methods. The 2-step method of Bandpass filter + Phansalkar local threshold (LT) showed the best performance with mean pixel accuracy of 86.9% ± 6.8%, area under the curve of 0.826, sensitivity of 79.0%, and specificity 86.1%. Conclusions Bandpass filter + Phansalkar LT was the best method for VD measurement in conjunctival OCTA. Most commonly reported threshold methods showed unsatisfactory agreement. There is a need in the OCTA field for a standardized method to allow comparison between different studies. Translational Relevance The proposed threshold method using a widely accessible and commonly used software provides an accurate VD measurement for future OCTA studies.

Novel Application of Conjunctival Anterior Segment Optical Coherence Tomography Angiography to Assess Ocular Redness

“…Each image was then binarized using open source Fiji software, 22 based on the previously published thresholding method. 23 In brief, each image was converted into a binary image using a combined process where a bandpass filter is initially applied to remove background noise, and then, an automated local threshold method was performed for image binarization (Fig. 1A-C).…”

Application of ImageJ in Optical Coherence Tomography Angiography (OCT‐A): A Literature Review

“…Thresholding techniques involve setting a threshold value for the intensity of pixels in an image. This threshold value separates the image into binary regions, where pixels with intensities above the threshold are assigned one value, usually white, and pixels below the threshold are assigned another value, usually black [ 34 ]. There are three ways of thresholding images: (1) global thresholding, (2) local thresholding, and (3) complex thresholding.…”

“…In order to have standard quantitative data, it seems better to make unification in choosing the best algorithms in studies because it is impossible to compare the results when the methods and data of studies are different. Some studies suggest local thresholding in order to make binary images [ 34 , 39 , 40 ]. Laiginhas et al in the study of choriocapillaris evaluated different thresholding methods to assess their repeatability: (1) the local method (Niblack, mean, and Phansalkar) and (2) the global method (default, mean, and Otsu).…”

“…The higher the steps, the more the filters used to reduce the noise. Finally, after analysis of the results, they mentioned that the bandpass filter plus Phansalkar (local) threshold (two steps thresholding) is the best way to binarize images [ 34 ]. After the threshold is applied, the binary image can be further processed to remove noise or fill in any gaps in the segmented vessels.…”

“…One critical feature of OCT-A images is measuring the vascular density, which is the number of pixels representing blood vessels divided by the total pixels in the analysis area [ 34 , 75 ]. Vascular density is a vital biomarker in ophthalmology, as it provides information on retinal perfusion and is associated with various ocular diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration [ 49 , 76 , 77 ].…”