Optical coherence tomography (OCT) has recently become one of the primary methods for noninvasive probing of the human retina. The pseudoimage formed by OCT (the so-called B-scan) varies probabilistically across pixels due to complexities in the measurement technique. Hence, sensitive automatic procedures of diagnosis using OCT may exploit statistical analysis of the spatial distribution of reflectance. In this paper, we perform a statistical study of retinal OCT data. We find that the stretched exponential probability density function can model well the distribution of intensities in OCT pseudoimages. Moreover, we show a small, but significant correlation between neighbor pixels when measuring OCT intensities with pixels of about 5 microm. We then develop a simple joint probability model for the OCT data consistent with known retinal features. This model fits well the stretched exponential distribution of intensities and their spatial correlation. In normal retinas, fit parameters of this model are relatively constant along retinal layers, but varies across layers. However, in retinas with diabetic retinopathy, large spikes of parameter modulation interrupt the constancy within layers, exactly where pathologies are visible. We argue that these results give hope for improvement in statistical pathology-detection methods even when the disease is in its early stages.

A self-routing multi-logN permutation network is presented and studied. This network has 3log/sub 2/ N-2 depth and N(log/sub 2//sup /spl gamma//N)(3log/sub 2/, N-2)/2 nodes, where N is the number of network inputs and /spl gamma/ a constant very close to 1. A parallel routing algorithm runs in 3log/sub 2/N-2 time on this network. The overall system (network and algorithm) can work in pipeline and it is asymptotically nonblocking in the sense that its blocking probability vanishes when N increases, hence the quasi-totality of the information synchronously arrives in 3log/sub 2/N-2 steps at the network outputs. This network presents very good fault tolerance, a modular architecture, and it is suitable for information exchange in very large scale parallel processors and communication systems.>

A self-routing multi-log-N permutation network is presented and studied. This network has log/sub 2/N depth and N(log/sub 2//sup 2/N+log/sub 2/N)/2 nodes, where N is the number of network inputs. Its parallel routing algorithm runs in log/sub 2/N time. The network architecture guarantees that only a negligible quantity of information is blocked, while the quasi-totality of the information synchronously reaches the network outputs in log/sub 2/N steps. This network works in pipeline, it has a modular architecture suitable for very large N, it can be used as very high performance fast packet switching fabric, and it is suitable for information exchange in very-large-scale multiprocessor systems.>