Segmentation of intra-retinal layers in 3D optic nerve head images

Abstract

Spectral-Domain Optical Coherence Tomography (SD-OCT) is a non-invasive imaging modality, which provides retinal structures with unprecedented detail in 3D. In this paper, we propose an automated segmentation method to detect intra-retinal layers in SD-OCT images around optic nerve head acquired from a high resolution RTVue-100 SDOCT (Optovue, Fremont, CA, USA). This method starts by removing all the OCT imaging artifacts including the speckle noise and enhancing the contrast between layers using the 3D nonlinear anisotropic. Afterwards, we combine the level set method, k-means and MRF method to segment three intra-retinal layers around optical nerve head. The segmentation results show that our method can effectively delineate the surfaces of the retinal tissues in the noisy 3D optic nerve head images. The signed and unsigned significant differences between the segmentation results and the ground truth over optic nerve head B-scans are 1.01±1.13 and 1.93±2.21.