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Retina Vessels Detection Algorithm for Biomedical Symptoms Diagnosis

by Nishu Bansal, Maitreyee Dutta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 71 - Number 20
Year of Publication: 2013
Authors: Nishu Bansal, Maitreyee Dutta
10.5120/12606-9484

Nishu Bansal, Maitreyee Dutta . Retina Vessels Detection Algorithm for Biomedical Symptoms Diagnosis. International Journal of Computer Applications. 71, 20 ( June 2013), 41-46. DOI=10.5120/12606-9484

@article{ 10.5120/12606-9484,
author = { Nishu Bansal, Maitreyee Dutta },
title = { Retina Vessels Detection Algorithm for Biomedical Symptoms Diagnosis },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 71 },
number = { 20 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume71/number20/12606-9484/ },
doi = { 10.5120/12606-9484 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:36:11.761718+05:30
%A Nishu Bansal
%A Maitreyee Dutta
%T Retina Vessels Detection Algorithm for Biomedical Symptoms Diagnosis
%J International Journal of Computer Applications
%@ 0975-8887
%V 71
%N 20
%P 41-46
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a method for blood vessel detection in digital retinal images. The method uses fuzzy logic approach with block wise gridding. It uses an adaptive approach for vessel detection. The segmentation is produced by classifying each pixel of the image as vessel or nonvessel. The performance of the proposed methodology is evaluated on the publicly available DRIVE database. It also contains manually labeled images by experts. Performance of this method on set of test images shows significant improvement than other solutions present in the literature. The method proves especially accurate results for vessel detection in DRIVE images. The method is simple and has fast implementation. It shows effectiveness and robustness with different image conditions. The vessel detection performance has a sensitivity of 0. 8653 with specificity 0. 9833. The accuracy of the method is 0. 9728 for Drive database. This blood vessel detection and segmentation technique can play a useful clinical role in an automated retinopathy analysis system.

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Index Terms

Computer Science
Information Sciences

Keywords

Diabetic retinopathy block wise gridding retinal image vessels segmentation

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