Improvement of thin retinal vessel extraction using mean matting method

Abstract

AbstractIn this paper, a new mean matting method based on mean correlation is proposed to extract thin blood vessels precisely. The proposed algorithm is the combination of both supervised and unsupervised method. The supervised methodology performs well in extracting thick blood vessels; however, thin vessels are not precisely extracted. Even the capability of unsupervised method is better in extracting thin vessels; it has some artifacts in the output. The proposed method combines the advantages of both supervised and unsupervised method to extract vessel regions more precisely irrespective of their thickness. Using supervised methodology, thick blood vessels are extracted by training the classifier with the significant features describing the vessel regions. Trimap is generated on the unsupervised output and mean correlation is computed for all unknown pixels in the trimap to classify those pixels into vessels or background. The proposed matting method has less computational complexity compared to other existing matting methods. The performance of the proposed method is evaluated in detail on DRIVE and STARE datasets.