DecomposedExcitatory Antagonistic Model for Reliable Retinal Vessel Detection A new retinal vessel detection method is introduced using an antagonistic model, consisting of the excitatory and inhibitory regions. It utilizes the model profile containing important image features such as lines and curves to detect retinal vessels. The excitatory area is analyzed into several subregions with multiple directions to decompose the local vessel structure, resulting in several profiles that include various vessel trees. Also, the strength of the inhibitory region is adaptively varied to differentiate vessel pixels from background while decreasing false detection. Then, each profile is linked to a multibound relaxed median operator for noise removal, and a logistic function for providing a soft transition between vessel and nonvessel regions. The final vessel tree is produced by fusing all profiles. The proposed method is evaluated on highresolution and lowresolution fundus imaging databases (HRF and DRIVE), on which it outperforms stateoftheart methods.

Retinal Vessel Segmentation Based on Multiscale Line Detection and Morphological Transformation Retina is an important part of human eyes, analyses of retinal vessels are preferable to the early prevention, detection and treatment of cardiovascular diseases. In this paper, we propose an effective unsupervised segmentation method. Firstly, the image is preprocessed to reduce interference of external factors; Secondly, the preprocessed image is enhanced to suppress the optic disc and background interference; Finally, based on the method of multiscale line detection, the blood vessels images are obtained. We evaluate our algorithm on the public datasets DRIVE and STARE, the experimental results show that the accuracy is about 95.90% and 96.11%, the sensitivity is 76.70% and 75.86%, the specificity is 95.20% and 95.56% respectively. Compared with other unsupervised methods, the proposed algorithm has a better segmentation effect.