Flow Void Analysis Using Different Thresholding Methods on a Choriocapillaris Optical Coherence Tomography Angiography Image Complemented with a Structural En Face Image.

Korean journal of ophthalmology : KJO Pub Date : 2024-02-01 Epub Date: 2023-12-15 DOI:10.3341/kjo.2023.0110
Young Joo Lee, Soon-Young Hwang, Cheolmin Yun
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Abstract

Purpose: To investigate the flow characteristics using different thresholding methods on a choriocapillaris optical coherence tomography angiography (OCTA) image complemented with a structural En Face image.

Methods: The 42 choriocapillaris OCTA images from healthy subjects were obtained with swept-source OCTA device and the 3 × 3-mm area OCTA images were processed with ImageJ. Using a raw choriocapillaris OCTA image and structural En Face image, we adjusted the different structural signal intensity. Then, the raw images and adjusted images were binarized with a global threshold and an auto local threshold using the Phansalkar method at 1- or 2-intercapillary distance. Then, the mean area, number, and size of the flow void, were compared among the images using different thresholding methods.

Results: Mean flow void area, number, and size were different according to the different binarization method both in raw and adjusted images (all p < 0.001). The mean flow void area analyzed with global threshold method were well correlated with those with auto local threshold method both in raw and adjusted images (all intraclass correlations, >0.929).

Conclusions: Flow void features varied according to the different binarization methods but showed good correlation. The flow void characteristics according to the different binarization methods should be considered for the analysis of the choriocapillaris OCTA images complemented with a structural En Face image.

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使用不同的阈值分析方法对绒毛膜 OCTA 图像和结构面图像进行流空分析。
背景:在绒毛膜光学相干断层血管成像(OCTA)图像上使用不同的阈值法研究血流特征:目的:采用不同的阈值处理方法,研究绒毛膜光学相干断层血管造影(OCTA)图像与结构面图像互补后的血流特征:使用扫源 OCTA 设备(蔡司 Plex-Elite)获取了 42 张健康受试者的绒毛膜 OCTA 图像,并使用 Image J 处理了 3 x 3 mm 面积的 OCTA 图像。然后,使用全局阈值和自动局部阈值对原始图像和调整后的图像进行二值化处理,并使用 Phansalkar 选项对 1 个毛细血管间距(ICD)或 2 个毛细血管间距(ICD)进行二值化处理。然后,使用不同的阈值方法比较图像中血流空洞的平均面积、数量和大小:原始图像和调整图像的平均血流空洞面积、数量和大小因二值化方法的不同而不同(均为 P <0.001)。采用全局阈值法分析的平均血流空洞面积与采用自动局部阈值法分析的平均血流空洞面积在原始图像和调整图像中均具有良好的相关性(所有图像,ICC > 0.929):结论:不同二值化方法的血流空洞特征各不相同,但相关性良好。在分析绒毛冠状动脉 OCTA 图像并辅以结构面图像时,应考虑不同二值化方法的血流空洞特征。
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