Abstract
Retinal vessel segmentation is a method of analyzing the tree like vessel structure in a fundus image. This is to support the diagnosis of different ophthalmology diseases, such as: arteriosclerosis, retinal occlusions and diabetic retinopathy. In this work, we suggest an automated retinal vessel segmentation scheme which involves three steps. First, the retinal image enhancement using Bowler Hat Transform (BHT) for improving the appearance of the image and for converting it to a form better suited for further analysis. Second, the use of matched filter for detection of retinal vessels from the enhanced image using a Gaussian shaped function. Third, the use of joint relative entropy (JRE) for obtaining an optimal threshold value for thresholding. A set of standard fundus images (from DRIVE dataset) are used for testing the efficiency of the suggested scheme. Experimental outcomes show the superiority of the proposed method in terms of the average accuracy, specificity and sensitivity. The efficiency of the suggested scheme is evaluated in comparison with the existing methods. It is to be noted that the suggested scheme is found to be substantially better.
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Junaid, I., Jena, U., Mishro, P.K. (2021). Retinal Vessel Segmentation Using Joint Relative Entropy Thresholding on Bowler Hat Transform. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_12
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