Translation Histogram Based Hierarchical Algorithm for 3-D Optic Nerve Head Modeling

This paper describes a translation histogram based, hierarchical algorithm for automated three-dimensional (3-D) optic nerve head (ONH) modeling from stereoscopic ONH photographs. Recovering the depths in featureless region is still one of the problems in previous studies of 3-D ONH reconstruction. The proposed algorithm hierarchically optimized and modeled the peripheral ONH surface to solve this problem. The algorithm has various steps consisting of disparity detection, hierarchical surface modeling, weighted fusing, and depth calibration. Dual-registration algorithm is firstly applied to precisely detect the matching points which are then converted into disparities. The peripheral ONH surface is initialized and refined through hierarchical modeling and optimization from the disparities. The final 3-D ONH model is generated by fusing the modeled peripheral ONH surface and the depths measured from dual-registration together with the interpolation. The true depth is obtained after calibration of eye lens through the axial length information. The experimental results showed the proposed algorithm could successfully generate 3-D ONH model, and get good consistency with human expert in cup-to-disc (C/D) ratio evaluation. The algorithm indicates the potential usefulness for 3-D ONH modeling and evaluation.