Improving automatic exudate detection based on the fusion of the results of multiple active contours

In this paper, we recommend an algorithm for exudate detection which is based on the combination of active contours obtained for the image after different preprocessing algorithms. First, we extract the candidate regions of exudates with grayscale morphology and then we minimize the Chan-Vese energy function to achieve the nearly accomplished boundary on nine different pre-processed images. Considering the combinations of the nine regions derived from the results of the active contours we select the one as the final exudate region that meets the most of some textural constraints. For this task, we apply machine learning based classification with using a training data set to determine these constraints. We considered the publicly available DiaretDB1 color fundus image set, where our method achieved higher accuracy than other state-of-the-art exudate detectors regarding both sensitivity and correct boundary extraction.