Precise localisation of an optic disk (OD) in the retinal images is one of the most important problems in the ophthalmic image processing. Although a considerable progress has been made towards a computerised solution of the problem, the numerical algorithms often fail on retinal images characterised by poor quality. Therefore, the authors propose a new method suitable for low‐quality images based on exploiting the convergence of the blood vessels to the OD. The novelty of the proposed techniques includes clustering the vessels endowed with a novel correction procedure and the vessel transform (VT) which measures the distance to the main clusters. The algorithm is integrated into the scale‐space (SS) analysis to detect the boundary of the OD. The integrated method is called SS algorithm with VT (SSVT). SSVT has been tested on retinal images from two databases with fair and poor images against the fuzzy convergence (FC) method and a modification of the circular transform proposed by Lu. The absolute improvement on sensitivity of SSVT against FC and Lu's are up to 12.37% and 8.18%. Bigger improvements of SSVT in terms of positive predictive value are up to 37.46% and 30.84% against FC and Lu's, respectively.

Higher-order active contours or snakes show much promise for the extraction of complex objects from noisy imagery. These models provide an elegant mathematical framework for specifying the desired properties of target objects through energy functionals that can be minimized with standard optimization techniques. However, techniques to allow quadratic snakes to change topology during segmentation have not been fully exploited. Additionally, external forces for improving convergence of quadratic snakes have similarly yet to be explored. In this article, we propose a model that allows multiple quadratic snakes to split, merge, and disappear. Although the separate components of our approach have been introduced elsewhere by Cohen (Citation1991), Xu and Prince (Citation1997), and Rochery et al. (Citation2006), this article is the first comprehensive empirical study of their performance on real-world complex network extraction tasks. We analyze the applicability of the model to road extraction from satellite images that vary in complexity from simple networks to large networks with multiple loops. We also analyze the effects of external forces enhanced by oriented filtering, gradient vector flow fields, and Canny edge detection. In a series of experiments, we found that the multiple cooperating quadratic snake model performs well on complex, noisy images. Our experiments also establish a performance improvement when the proposed quadratic model is coupled with the Canny-based gradient vector flow technique.