Segmenting retinal vessels with revised top-bottom-hat transformation and flattening of minimum circumscribed ellipse

Retinal vessel automatic segmentation plays a great important role for analyzing fundus pathologies like diabetes, retinopathy, and hypertension. In this paper, a novel unsupervised method to automatically extract the vessels from fundus images is introduced. The method proposed a new vessel enhancement approach that we called revised top-bottom-hat transformation for removing the bright lesions for further enhancing vessels in a fundus image, and provides a novel feature that we call flattening of minimum circumscribed ellipse for recognizing a vessel. This method was tested on two publicly available databases DRIVE and STARE, and achieved an average accuracy of 0.9446 and 0.9503, respectively. For pathological cases, the approach reached an accuracy of 0.9435 and 0.9439, respectively. The time complexity approaches (O(n)), which is significantly lower than the state-of-the-art method.