Novel approach for anterior chamber angle analysis: Anterior Chamber Angle Detection with Edge Measurement and Identification Algorithm (ACADEMIA)

Objective: To describe a novel approach to measuring anterior chamber angle dimensions and configurations. Methods: Sixty-nine images were selected randomly from the ultrasound biomicroscopic image database to develop the algorithm. Thirty images were selected for further analyses. The value of each pixel of the 8-bit grayscale ultrasound biomicroscopic images was quantized into 0 (black) or 1 (white), and the edge points outlining the angle were detected and fitted with straight lines. The dimensions and profiles of anterior chamber angles were then measured. Results: The algorithm failed to identify the edge points correctly in 8 (11.6%) of 69 images because of strong background noise. Three basic types of angle configuration were identified based on the derived angle profiles: constant, increasing, and decreasing, which corresponded to flat, bowed forward, and bowed backward iris contours, respectively. The angle measurements demonstrated high correlation with trabecular-iris angle and angle opening distance 500 (calculated as the distance from the corneal endothelium to the anterior iris surface perpendicular to a line drawn at 500 μm from the scleral spur). The strongest association was found between the averaged angle derived from the angle profile and the angle opening distance 500 (r=0.91). Conclusion: The proposed algorithm has high correlations with angle opening distance and trabecular-iris angle with the added advantages of being fully automated, reproducible, and able to capture the characteristic angle configurations. However, good-quality ultrasound biomicroscopic images with high signal-to-noise ratio are required to identify the edge points correctly.