Abstract
Conference Title: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Conference Start Date: 2016, Nov. 30 Conference End Date: 2016, Dec. 2 Conference Location: Gold Coast, Australia Retinal vessel segmentation plays a key role in the detection of numerous eye diseases, and its reliable computerised implementation becomes important for automatic retinal disease screening systems. A large number of retinal vessel segmentation algorithms have been reported, primarily based on three main steps including uniforming background, using the second-order Gaussian detector and applying binarization. These methods though improve the accuracy levels, their sensitivity to low-contrast in vessels still needs attention. In this paper, some contrast-sensitive approaches are discussed and embedded in the conventional algorithms, resulting in improved sensitivity for a given retinal vessel extraction technique. The proposed method gives good performance on both publicly databases with the accurate vessel extraction on STARE database. The proposed unsupervised method achieves the accuracy of 94.41%, much better than some existing unsupervised methods and comparable to some supervised methods. Its efficiency with different image conditions, together with its simplicity and fast operation, makes the blood vessel segmentation application suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.