Optical coherence tomography image segmentation

Optical coherence tomography (OCT) is a three-dimensional non-invasive imaging technique that can generate images of the eye at microscopic level to help diagnosis of eye diseases. However, OCT images often suffer from inhomogeneity and are corrupted by speckle noise, posing challenges to automated OCT image segmentation and analysis. In this paper, a novel method is proposed to segment retinal layers in OCT images. The proposed method uses a coarse-to-fine approach to segmentation and includes: (1) A variational retinex model that can enhance the details as well as correct the intensity in-homogeneities; (2) An anisotropic coherent enhancing diffusion that can remove speckle noise and simultaneously connect the interrupted retinal layers; (3) A nonlinear isotropic filter that smooths the processed OCT image and leads to the initial coarse segmentation; (4) A high-pass unsharp masking filter that highlights the remaining layers and gives the fine segmentation. Afterwards, all the retinal layers that can be seen by human eyes are segmented accurately using common edge detection of region based segmentation algorithms. Extensive experiments results validate the effectiveness and performance of the proposed method.