Segmentation of the Surfaces of the Retinal Layer from OCT Images

Abstract

Abstract. We have developed a method for the automated segmentation of the internal limiting membrane and the pigment epithelium in 3-D OCT retinal images. Each surface was found as a minimum s-t cut from a geometric graph constructed from edge/regional information and a priori-determined surface constraints. Our approach was tested on 18 3-D data sets (9 from patients with normal optic discs and 9 from patients with papilledema) obtained using a Stratus OCT-3 scanner. Qualitative analysis of surface detection correctness indicates that our method consistently found the correct surfaces and outperformed the proprietary algorithm used in the Stratus OCT-3 scanner. For example, for the internal limiting membrane, 4% of the 2-D scans had minor failures with no major failures using our approach, but 19% of the 2-D scans using the Stratus OCT-3 scanner had minor or complete failures.

Human Photoreceptor Outer Segments Shorten During Light Adaptation

“…29,31,32 Briefly, we use a graph-searchÀbased formulation of simultaneous, globally optimal segmentation of multiple 3-D surfaces (minimum-cost set), in a vertex-weighted graph, to segment multiple surfaces simultaneously. Both regional and edge-based costs, as well as varying feasibility constraints, are incorporated in the graph using a supervised approach for determining the feasibility constraints.…”

Extended correction model for retinal optical imaging

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“…By sampling these polynomials, we obtain a new image (1) . One then fits 5 degree polynomials to the columns of (0) , which yields an image (1) . The mean of (1) and (1) is denoted by (1) .…”

“…One then fits 5 degree polynomials to the columns of (0) , which yields an image (1) . The mean of (1) and (1) is denoted by (1) . One then iterates this procedure with (1) and obtains the iterative scheme…”

“…The mean of (1) and (1) is denoted by (1) . One then iterates this procedure with (1) and obtains the iterative scheme…”