Visualization of medical data requires the extraction of surfaces that represent the boundaries of objects of interest. This paper describes a method that combines finding these boundaries accurately and ensuring that this surface consists of high quality triangles. The latter is important for subsequent visualization and simulation. We show that the surfaces created using this method are both accurate and have good quality triangles.

Wedge shaped defects of the retinal nerve fiber layer (RNFL) may occur in glaucoma. Currently, automatic detection of wedge shaped defects in Scanning Laser Polarimetry images of the retinal nerve fiber layer is unavailable; an automatic classification is currently based only on global parameters, thereby ignoring important local information. Our method works by a modified dynamic programming technique that searches for locally strong edges with a preference for straight edges. These edges are initially classified based on their strength and then combined into wedge shaped defects. The results of our method on a limited set of 45 images yields a sensitivity of 88% and a specificity of 92%. More importantly, it shows that it is possible to automatically extract local RNFL defects such as wedges.