Efficient optic cup detection from intra-image learning with retinal structure priors.

Keyphrases

• Retinal Structure 100%
• Fundus Image 100%
• Optic Cup 100%
• Training Samples 100%
• Image Learning 100%
• Structural Prior 100%
• Structure Prior 100%
• Superpixel 50%
• State-of-the-art Techniques 50%
• Order of Accuracy 50%
• Image Components 50%
• Glaucoma 50%
• Clinical Datasets 50%
• Non-overlapping 50%
• Overlap Ratio 50%
• Superpixel-based 50%
• Cup-to-disc Ratio 50%
• Glaucoma Diagnosis 50%
• Learning Framework 50%
• Sliding Window 50%
• Ratio Error 50%
• Diagnostic Ratios 50%
• Learning Process 50%
• Local Context 50%
• Refinement Scheme 50%
• Pixel-based 50%
• Computational Savings 50%
• Disc Position 50%
• Classification Refinement 50%
• Cup Position 50%
• Prior Context 50%
• Classifier Learning 50%
• Speedup Ratio 50%

Computer Science

• Superpixel 100%
• Training Sample 100%
• Learning Framework 50%
• Learning Process 50%
• Sliding Window 50%

Engineering

• Ground Truth 100%
• Test Image 100%
• State-of-the-Art Technique 100%