Is pseudophakia a risk factor for neovascular age-related macular degeneration?

PURPOSE: To examine the possible association between pseudophakia and neovascular age-related macular degeneration (AMD). METHODS: Reports of all patients undergoing fluorescein angiography in the authors' department over a 6-year period were retrospectively reviewed. Four hundred ninety-nine patients with recent onset of neovascular AMD in one eye and early age-related maculopathy (ARM) in the fellow eye were included in the study. Lens status (phakic or pseudophakic) in both eyes at the time of onset of neovascular AMD and the time between cataract surgeries (if performed) and onset of neovascular AMD were determined. RESULTS: There was no significant difference in lens status between eyes with neovascular AMD and fellow eyes with early ARM (115/499 [23.0%] vs. 112/499 [22.4%] pseudophakic; P = 0.88, odds ratio 1.035, 95% CI 0.770-1.391). Subgroup analysis revealed no difference between the groups with large drusen, small drusen, or pigmentary changes only (respectively, 20.3% vs. 19.6% pseudophakic, P = 0.92; 20.5% vs. 23.3% pseudophakic, P = 0.84; 33.3% vs. 31.7% pseudophakic, P = 1.0). Pseudophakic eyes with neovascular AMD had not been pseudophakic for a significantly longer period at the time of onset of neovascular AMD than their pseudophakic fellow eyes at the same time point (225.9 +/- 170.4 vs. 209.9 +/- 158.2 weeks, P = 0.27). CONCLUSIONS: The results do not support the hypothesis that pseudophakia is a major risk factor for the development of neovascular AMD.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.