A variational approach to automatic segmentation of RNFL on OCT data sets of the retina

Optical coherence tomography (OCT) as a new imaging technology is gaining popularity in the diagnosis of ocular diseases. It enable clinicians to perform accurate, objective, and reproducible measurements of the retinal nerve fibre layer (RNFL) whose thickness is closely related to many ocular diseases. Automatic segmenting RNFL is a challenging image processing problem, which is a critical job for final thickness estimation. We modeled the OCT data sets as probability density fields and introduced a level set model to outline the RNFL region within the retina. We also introduced the symmetrized Kullback-Leibler distance to describe the difference of two density functions. The new approach can deal with the typical problems of OCT image analysis: speckle noise and faint structure in an efficient way.