Automated depth analysis of optic nerve head from stereo fundus images
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Early detection of structural damage to the optic nerve head (ONH) is critical in diagnosis of glaucoma, because such glaucomatous damage precedes clinically identifiable visual loss. Early detection of glaucoma can prevent progression of the disease and consequent loss of vision. The Gold standards of glaucoma detection imclude visual field test, intraocular pressure monitoring and stereo fundus photography. Stereo fundus photography is routinely used to detect subtle changes in the ONH. However, clinical evaluation of stereo fundus photographs suffers from inter- and intra-subject variability. Even the sophisticated optical instruments like the Heidelberg Retina Tomograph (HRT) have not been found to detect glaucoma any earlier than visual field loss. A semi-automated algorithm for quantitative representation of the optic disc and cup contours by computing accumulated disparities in the disc and cup regions from stereo fundus image pairs has already been developed using advanced digital image analysis methodologies. This method demonstrates high correlation among computer-generated and manually segmented cup to disc ratios in a longitudinal study. However, clinical usefulness of the proposed technique can only be tested by a fully automated algorithm. In this thesis, a fully automated algorithm for segmentation of optic cup and disc contours from corresponding stereo disparity information is presented. The system developed and presented in this thesis, takes only a stereo pair of fundus images as input and generates cup and disc contours automatically along with the three-dimensional visualization of the Optic Nerve Head in true color. Because this technique does not involve human intervention, it eliminates subjective variability encountered in currently used clinical methods and provides ophthalmologists with a cost-effective and quantitative method for detection of ONH structural damage for early detection of glaucoma.