Learning local vessel appearance models using structured sparsity

Vessel segmentation is a challenging task due to the complexity of vascular networks and limitations of imaging modalities to accurately capture thin structures. Analytical models based on geometric appearance and/or edge-based assumptions have been shown to be sub-optimal in segmenting vessels. In this paper, a novel approach for learning vessel appearance models from localized-vessel image patches is presented. This approach uses subspace clustering methods based on sparse representation of signals to identify local vessel appearance models from a training dataset. This paper also presents a hierarchical subspace clustering framework, which improves clustering speeds in presence of large number of subspaces. The preliminary results obtained for segmenting retinal vessel images using learned appearance models on the publicly available DRIVE database, yields an accuracy of 0.9268 at 0.1344 false detections and reduces the search space up to 80%.