Human Retina Segmentation from Optical Coherence Tomography

Background: The human retina is made up of ten layers. Measuring the thickness of these layers is an important step in detecting diseases such as macular degeneration, glaucoma, and diabetic retinopathy. Optical Coherence Tomography (OCT) is a non-invasive technique to generate cross-sectional imagery of ocular structures, enabling study of individual retina layers.

Solution: This project developed a solution for segmenting layers of the human retina from noisy OCT images. Retina layers were detected incrementally, with the boundary for each delineated while smoothly maneuvering around noisy pixels. The accuracy for each layer was measured during each step of the project and failure modes studied in order to discover areas for improvement.

Additional Applications: The active contour technique used for a smooth segmentation can be adapted to many other image segmentation applications. It especially excels in noisy images and can be tuned for different levels of smoothing.