TRAMLINE AND NP WINDOWS ESTIMATION FOR ENHANCED UNSUPERVISED RETINAL VESSEL SEGMENTATION

This paper presents a novel unsupervised vascular segmentation algorithm which is applied to retinal fundus images, however could be generalised to any two-dimensional vascular image. The algorithm presents a new fully automatic framework for vessel segmentation and comprises the following features: novel application of the NPWindows method for intensity distribution estimation on localised 'image patches'; specialised treatment of small vessels by transformation to the one-dimensional domain to ensure enhanced detection; and excellent accuracy (93.42%) as compared with the recent active-contour based method by Al-Diri et al. [1] (92.58%) on the public DRIVE retinal image database [2].