Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation.
Repository Usage Stats
Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.
Automatic Data Processing
Diagnostic Techniques, Ophthalmological
Image Processing, Computer-Assisted
Optics and Photonics
Tomography, Optical Coherence
More InfoShow full item record
Paul Ruffin Scarborough Associate Professor of Engineering
I am the director of the Vision and Image Processing (VIP) Laboratory. Along with my colleagues, we investigate how to improve early diagnostic methods and find new imaging biomarkers of ocular and neurological diseases in adults (e.g. age-related macular degeneration, diabetic retinopathy, Glaucoma, Alzheimer) and children (e.g. retinopathy or prematurity). We also develop automatic artificial intelligence machine learning and deep learning algorithms to detect/segment/quantify anatomical/patho
Clinical Associate in the Department of Ophthalmology
This author no longer has a Scholars@Duke profile, so the information shown here reflects their Duke status at the time this item was deposited.
Alphabetical list of authors with Scholars@Duke profiles.