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An improved vessel extraction scheme from retinal fundus images

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Abstract

Vessel extraction from retinal fundus images is essential for the diagnosis of different opthalmologic diseases like glaucoma, diabetic retinopathy and hypertension. It is a challenging task due to presence of several noises embedded with thin vessels. In this article, we have proposed an improved vessel extraction scheme from retinal fundus images. First, mathematical morphological operation is performed on each planes of the RGB image to remove the vessels for obtaining noise in the image. Next, the original RGB and vessel removed RGB image are transformed into negative gray scale image. These negative gray scale images are subtracted and finally binarized (BW1) by leveling the image. It still contains some granular noise which is removed based on the area of connected component. Further, previously detected vessels are replaced in the gray-scale image with mean value of the gray-scale image and then the gray-scale image is enhanced to obtain the thin vessels. Next, the enhanced image is binarized and thin vessels are obtained (BW2). Finally, the thin vessel image (BW2) is merged with the previously obtained binary image (BW1) and finally we obtain the vessel extracted image. To analyze the performance of our proposed method we have experimented on publicly available DRIVE dataset. We have observed that our algorithm have provides satisfactory performance with the sensitivity, specificity and accuracy of 0.7260, 0.9802 and 0.9563 respectively which is better than the most of the recent works.

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Notes

  1. DRIVE dataset is available at http://www.isi.uu.nl/Research/Databases/DRIVE/download.php.

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Authors and Affiliations

  1. St. Thomas’ College of Engineering and Technology, Kolkata, West Bengal, India

    Ranjit Ghoshal, Aditya Saha & Sayan Das

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  1. Ranjit Ghoshal
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  2. Aditya Saha
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  3. Sayan Das
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Correspondence to Ranjit Ghoshal.

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Ghoshal, R., Saha, A. & Das, S. An improved vessel extraction scheme from retinal fundus images. Multimed Tools Appl 78, 25221–25239 (2019). https://doi.org/10.1007/s11042-019-7719-9

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  • DOI: https://doi.org/10.1007/s11042-019-7719-9

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