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. 2021 May 7;8:657772. doi: 10.3389/fmed.2021.657772

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Copyright © 2021 Wang, Hu, Zhu, Su, Fang, Lin, Chen, Su, Ye, Ma, Zhang, Li, Feng, Sun, Zhang and Shentu.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Representative OCTA images of mCNV before and after cropping, binarisation, and skeletonisation in Patient #18, a 60-year-old female (refractive error −15.50 diopters) in the right eye. (A) OCTA image showing the entire thickness mCNV lesion after manual adjustment of segmentation. (B) The full extent of mCNV was manually delineated, and the mCNV area was measured by counting the pixels contained within the contour. (C) The Gaussian kernel was used to reduce the image noise and obtain a smooth image. (D,E) The binary OCTA image was formed by Frangi vesselness filter and local adaptive thresholding and used for calculating vessel area, fractal dimension and vessel lengths. Vessel density was calculated using vessel area and mCNV area. (F) Tagged Skeleton image was used to calculate the numbers of vessel junctions (white arrow). Junction density was calculated by dividing the vessel junction by the vessel length. Images (B–E) were acquired by MATLAB program and image (F) was acquired by IMAGE J software.