Computerized image analysis methods for retinal imaging are primarily of great interest and benefit as it provides significant information about the retinal vessels. Retinal image analysis techniques can be of pertinence for ophthalmologists and a stand-alone warning implement for determining the retinal disorders. This requires dedicated image processing algorithms to provide mathematical description about the region of interest. This paper presents an automated pixel processing-based retinal vessel extraction algorithm using modified Gabor functions and morphological operators. Color normalization is performed to make the algorithm adaptable to intra- and inter-image variabilities. Furthermore, the enhanced retinal vessels are subjected to automatic thresholding for vessel pixel classification. The proposed method is tested on a set of retinal images collected from the DRIVE database and subjected to robust performance analysis to evaluate the efficacy. The proposed algorithm achieved an average accuracy of 97.22%, sensitivity of 85.12% and specificity of 98.57%, which is comparably preferable to the well-known algorithms.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence