Qualitative comparison of choroidal vascularity measurement algorithms

• Main Authors: Mohammed A Rasheed, Niroj K Sahoo, Abhilash Goud, Kiran K Vupparaboina, Jay Chhablani
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2018-01-01
• Series: Indian Journal of Ophthalmology
• Subjects: Automated binarization; binarization; choroidal segmentation; choroidal vascularity index; manual binarization
• Online Access: http://www.ijo.in/article.asp?issn=0301-4738;year=2018;volume=66;issue=12;spage=1785;epage=1789;aulast=Rasheed
• ISSN: 0301-4738; 1998-3689

Purpose: To compare the accuracy of manual and automated binarization technique for the analysis of choroidal vasculature. Methods: This retrospective study was performed on a total of 98 eyes of 60 healthy subjects. Fovea-centered swept source optical coherence tomography (SS-OCT) scans were obtained and choroidal area was binarized using manual and automated image binarization technique separately. Choroidal vessel visualization in the binarized scans were subjectively graded (grades 0–100) by comparing them with the original OCT scan images by two masked graders. The subjective variability and repeatability was compared between two binarization method groups. Intergrader and intragrader variability was estimated using paired t-test. The degree of agreement between the grades for each observer and between the observers was evaluated using Bland–Altman plot. Results: The mean accuracy grades of the automatically binarized images were significantly (P < 0.001) higher (93.38% ± 1.70%) than that of manually binarized images (78.06% ± 2.92%). There was a statistically significant variability and poor agreement between the mean interobserver grades in the manual binarization arm. Conclusion: Automated image binarization technique is faster and appears to be more accurate in comparison to the manual method.