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27 February 2009 ARGALI: an automatic cup-to-disc ratio measurement system for glaucoma detection and AnaLysIs framework
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72603K (2009) https://doi.org/10.1117/12.810956
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Glaucoma is an irreversible ocular disease leading to permanent blindness. However, early detection can be effective in slowing or halting the progression of the disease. Physiologically, glaucoma progression is quantified by increased excavation of the optic cup. This progression can be quantified in retinal fundus images via the optic cup to disc ratio (CDR), since in increased glaucomatous neuropathy, the relative size of the optic cup to the optic disc is increased. The ARGALI framework constitutes of various segmentation approaches employing level set, color intensity thresholds and ellipse fitting for the extraction of the optic cup and disc from retinal images as preliminary steps. Following this, different combinations of the obtained results are then utilized to calculate the corresponding CDR values. The individual results are subsequently fused using a neural network. The learning function of the neural network is trained with a set of 100 retinal images For testing, a separate set 40 images is then used to compare the obtained CDR against a clinically graded CDR, and it is shown that the neural network-based result performs better than the individual components, with 96% of the results within intra-observer variability. The results indicate good promise for the further development of ARGALI as a tool for the early detection of glaucoma.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
J. Liu, D. W. K. Wong, J. H. Lim, H. Li, N. M. Tan, and T. Y. Wong "ARGALI: an automatic cup-to-disc ratio measurement system for glaucoma detection and AnaLysIs framework", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603K (27 February 2009); https://doi.org/10.1117/12.810956
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