Computer Aided Diagnostics and Pattern Recognition: Automated Glaucoma Detection

Glaucoma is one of the major causes for blindness with a high rate of unreported cases. To reduce this number, screening programs are performed. However, these are characterized by a high workload for manual and cost-intensive assessment. Computer-aided diagnostics (CAD) to perform an automated pre-exclusion of normals might help to improve program’s efficiency. This chapter reviews and discusses recent advances in the development of pattern recognition algorithms for automated glaucoma detection based on structural retinal image data. Two main methodologies for glaucoma detection are introduced: (i) structuredriven approaches that mainly rely on the automated extraction of specific medically relevant indicators, while (ii) data-driven techniques perform a generic machine-learning approach on entire image data blobs. Both approaches show a reasonable and comparable performance although they rely on different basic assumptions. A combination of these might further improve CAD for a more efficient and cost-sensitive workflow as a major proportion of normals will be excluded from unnecessary detailed investigations.