In this paper, a simple, fast, and efficient hybrid segmentation method is presented for extracting vessel structures in retinal fundus images. Basically, this hybrid approach combines circular and naive Bayes classifiers to extract blood vessels in retinal fundus images. The circular method samples pixels along the enlarging circles centered at the current pixel and classifies the current pixel as vessel or nonvessel. An elimination technique is then employed to eliminate the nonvessel fragments from the processed image. The naive Bayes method as a supervised technique uses a very small set of features to segment retinal vessels in retinal images. The designed hybrid method exploits the circular and Bayesian segmentation results together to achieve the best performance. The achieved performance of the segmentation methods are tested on DRIVE and STARE databases for evaluation. The proposed methods segment a retinal image within 1 s and achieve about 95% accuracy. The results also indicate that the proposed hybrid method is one of the simplest and efficient segmentation methods among the unsupervised and supervised methods in the literature.
Cemal REF implemented an hybrid segmentation method that combines Circular and Naive Bayes classifier to extract vessel network in retinal fundus images.
A simple hybrid method for segmenting vessel structures in retinal\\ fundus images
Turkish Journal of Electrical Engineering and Computer Sciences
Computer Science