Abstract
Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.
Highlights
Accurate detection of anatomical and pathological structures in Spectral Domain Optical Coherence Tomography (SDOCT) images is critical for the diagnosis and study of ocular diseases [1,2]
While the proposed technique can be generalized for segmenting any layered structure in images of any imaging modality, we focus on the segmentation of retinal layers in SDOCT images
Knowledge of these layer boundary positions allows for retinal layer thickness calculations, which are imperative for the study and detection of ocular diseases
Summary
Accurate detection of anatomical and pathological structures in Spectral Domain Optical Coherence Tomography (SDOCT) images is critical for the diagnosis and study of ocular diseases [1,2]. Knowledge of these layer boundary positions allows for retinal layer thickness calculations, which are imperative for the study and detection of ocular diseases.
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