Fovea and vessel detection via multi-resolution parameter transform

A multi-resolution, parallel approach to retinal blood vessel detection has been introduced that can also be used as a discriminant for fovea detection. Localized adaptive thresholding and a multi-resolution, multi-window Radon transform (RT) are utilized to detect the retinal vascular system. Multi-window parameter transforms are intrinsically parallel and offer increased performance over conventional transforms. Large vessels are extracted in low-resolution mode, whereas minor vessels are extracted in high-resolution mode further increasing computational efficiency. The image is adaptively thresholded and then the multi-window RT is applied at the different resolution levels. Results from each level are combined and morphologically processed to improve final performance. A systematic approach has been implemented to perform fovea detection. The algorithm relies on a probabilistic method to perform initial segmentation. The intensity image is re-mapped into probability space to detect areas with low-probability of occurrence. Intensity and probability information are coupled to produce a binary image that contains potential fovea candidates. The candidates are discriminated based upon their location within the blood vessel network.