Title :
Automatic recovery of the optic nervehead geometry in optical coherence tomography
Author :
Boyer, Kim L. ; Herzog, Artemas ; Roberts, Cynthia
Author_Institution :
Dept. of Electr. & Comput. Eng., Ohio State Univ., Columbus, OH, USA
fDate :
5/1/2006 12:00:00 AM
Abstract :
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.
Keywords :
Markov processes; biomedical optical imaging; diseases; eye; image segmentation; medical image processing; optical tomography; Markov model; automatic optic nervehead geometry recovery; choroid-nervehead boundary; eyes; glaucoma diagnosis; nerve fiber; neural tissue; ophthalmic imaging; optic cup; optic nervehead segmentation; optical coherence tomography; retinal-nervehead surface; Biological tissues; Biomedical optical imaging; Current measurement; Geometrical optics; Monitoring; Nerve fibers; Optical imaging; Pathology; Solid modeling; Tomography; Contour segmentation; Markov models; glaucoma; gradient descent; image segmentation; optic cup; optic disk; optic nervehead; optical coherence tomography; Algorithms; Artificial Intelligence; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Optic Disk; Pattern Recognition, Automated; Phantoms, Imaging; Reproducibility of Results; Sensitivity and Specificity; Tomography, Optical Coherence;
Journal_Title :
Medical Imaging, IEEE Transactions on
DOI :
10.1109/TMI.2006.871417