Automated segmentation of intramacular layers in Fourier domain optical coherence tomography structural images from normal subjects.

Segmentation of optical coherence tomography (OCT) cross-sectional structural images is important for assisting ophthalmologists in clinical decision making in terms of both diagnosis and treatment. We present an automatic approach for segmenting intramacular layers in Fourier domain optical coherence tomography (FD-OCT) images using a searching strategy based on locally weighted gradient extrema, coupled with an error-removing technique based on statistical error estimation. A two-step denoising preprocess in different directions is also employed to suppress random speckle noise while preserving the layer boundary as intact as possible. The algorithms are tested on the FD-OCT volume images obtained from four normal subjects, which successfully identify the boundaries of seven physiological layers, consistent with the results based on manual determination of macular OCT images.