SAHF: Unsupervised texture-based multiscale with multicolor method for retinal vessel delineation

Abstract

Automatic vessel delineation has been challenging due to complexities during the acquisition of retinal images. Although, great progress have been made in this field, it remains the subject of on-going research as there is need to further improve on the delineation of more large and thinner retinal vessels as well as the computational speed. Texture and color are promising, as they are very good features applied for object detection in computer vision. This paper presents an investigatory study on sum average Haralick feature (SAHF) using multi-scale approach over two different color spaces, CIElab and RGB, for the delineation of retinal vessels. Experimental results show that the method presented in this paper is robust for the delineation of retinal vessels having achieved fast computational speed with the maximum average accuracy of 95.67% and maximum average sensitivity of 81.12% on DRIVE database. When compared with the previous methods, the method investigated in this paper achieves higher average accuracy and sensitivity rates on DRIVE.