Coarse to fine segmentation of Stargardt rings using an expert guided dual ellipse model

Computer aided diagnosis in the medical image domain requires adaptive knowledge-based models to handle uncertainty, ambiguity, and noise. We propose an expert guided coupled dual ellipse model in a coarse to fine energy minimization framework. In our approach we enforce subspace model constraints by fusing domain knowledge and model information to guide the segmentation process on the fly. We apply our method to the task of retinal Stargardt segmentation a disease that manifests itself in a ring like structure around the macula. Quantitative evaluations on synthetic and real data sets show the performance of our framework. Experimental results demonstrate that our framework performance well with an area under the ROC curve of 0.93.