Automatic recovery of the optic nervehead geometry in optical coherence tomography

• Published in: IEEE transactions on medical imaging Vol. 25; no. 5; pp. 553 - 570
• Main Authors: Boyer, K.L., Herzog, A., Roberts, C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2006; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Biological tissues; Biomedical optical imaging; Contour segmentation; Current measurement; Geometrical optics; Geometry; glaucoma; gradient descent; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; image segmentation; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Markov models; Monitoring; Nerve fibers; optic cup; optic disk; Optic Disk - cytology; optic nervehead; optical coherence tomography; Optical imaging; Optics; Pathology; Pattern Recognition, Automated - methods; Phantoms, Imaging; Reproducibility of Results; Sensitivity and Specificity; Solid modeling; Studies; Tomography; Tomography, Optical Coherence - instrumentation; Tomography, Optical Coherence - methods
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2006.871417

Optical coherence tomography (OCT) uses retroreflected light to provide micrometer-resolution, cross-sectional scans of biological tissues. OCT's first application was in ophthalmic imaging where it has proven particularly useful in diagnosing, monitoring, and studying glaucoma. Diagnosing glaucoma is difficult and it often goes undetected until significant damage to the subject's visual field has occurred. As glaucoma progresses, neural tissue dies, the nerve fiber layer thins, and the cup-to-disk ratio increases. Unfortunately, most current measurement techniques are subjective and inherently unreliable, making it difficult to monitor small changes in the nervehead geometry. To our knowledge, this paper presents the first published results on optic nervehead segmentation and geometric characterization from OCT data. We develop complete, autonomous algorithms based on a parabolic model of cup geometry and an extension of the Markov model introduced by Koozekanani, et al. to segment the retinal-nervehead surface, identify the choroid-nervehead boundary, and identify the extent of the optic cup. We present thorough experimental results from both normal and pathological eyes, and compare our results against those of an experienced, expert ophthalmologist, reporting a correlation coefficient for cup diameter above 0.8 and above 0.9 for the disk diameter.