Spatial enhancement scheme with supervised pixel classification for blood vessel detection in retinal image

Retinal blood vessel segmentation is significant in proper detection of vascular anomalies manifested in different retinal pathologies. Perfect knowledge on blood vessel location is necessary for automated detection of retinal diseases. However, accurate identification of blood vessel locations by eye inspection is extremely difficult especially in faded regions or very thin vessel regions. In this thesis, a two-stage automatic vessel detection algorithm is proposed which involves rule based candidate vessel selection algorithm at the first stage followed by a post-processing scheme and a supervised classification algorithm in the second stage. In order to obtain enhanced vessel region, in the preprocessing scheme, first, spatial adaptive median filtering is introduced which can reduce noise generated by nonhomogeneous background and then the morphological Top-Hat transform is used for further background homogenization for vessel enhancement. A gradient based k-neighborhood (for k=1, 2, 3)) bidirectional spatial search method is proposed to select vessel candidates from preprocessed green plane of retinal image. A post-processing scheme based on spatial similarity and connectivity is employed to finalize the vessel candidate selection. Instead of pixel by pixel classification of the whole retinal image, a supervised classification scheme is developed where only some critical candidate pixels are tested using linear discriminant based classifier. The idea of such a selective classification offers huge computational savings. For feature extraction, both spatial and spectral features of the subregion centered on test pixel and 8-connected spatially shifted subregions with respect to the center pixel are considered. Since feature extraction is carried out on a larger block in comparison to the gradient search operation, in the preprocessing scheme, sequential morphological opening (filtering) operation in TopHat transform and background homogenization via shade correction are included. In supervised classification, instead of selecting training pixels by eye inspection, universal trainer selection algorithm is proposed based on principle of connectivity which is verified by discriminating feature characteristics obtained by selected pixels. Extensive simulation is carried out on some retinal image databases and it is found that a satisfactory performance is obtained by using the algorithm.