Image processing operations which we have used in the proposed approach are basically divided into three major stages viz; pre processing, Fréchet matched filter and post processing. In pre processing, principle component analysis (PCA) method is used to convert color image into grayscale image thereafter contrast limited adaptive histogram equalization (CLAHE) is applied on obtained grayscale to get enhanced grayscale image. In Fréchet matched filter, exhaustive experimental tests are conducted to choose optimal values for both Fréchet function parameters and matched filter parameters to design new matched filter. In post processing, entropy based optimal thresholding technique is applied on obtained MFR image to get binary image followed by length filtering and masking methods are applied to generate to a clear and whole vascular tree.
For evaluation of the proposed approach, quantitative performance metrics such as average specificity, average sensitivity and average accuracy and root mean square deviation (RMSD) are computed in the literature. We found the average specificity 97.24%, average sensitivity 72.78%, average accuracy 95.09% for STARE dataset while average specificity 97.61%, average sensitivity 73.07%, average accuracy 95.44% for DRIVE dataset. Average RMSD values are found 0.07 and 0.04 for STARE and DRIVE databases respectively.
From experimental results, it can be observed that our proposed approach outperforms over latest and prominent works reported in the literature. The cause of improved performance is due to better matching between vessel profile and Fréchet template.
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