TY - GEN
T1 - Segmentation of Blood Vessels by Local Analysis of 2D Image Patches
AU - Sridhar, Harsha
AU - Kumar, J. R.Harish
N1 - Funding Information:
This work is supported by the Science and Engineering Research Board (SERB) - Teachers Associateship for Research Excellence (TARE) Fellowship (No. TAR/2019/000037).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Retinal vasculature can be considered a major indicator of various opthalmologic disorders and diseases. Therefore, it is important to accurately detect and segment the retinal blood vessels. We present a novel technique for the segmentation of arteries and veins from a retinal fundus image. Local 2D image patch-based analysis using intensity gradients is used as the basis for detection of vessels. Two stages of preprocessing are implemented before detection for ease of approach. The proposed model carries several advantages over the state-of-the-art. The computational cost of the proposed model is low. Also, the average execution time per image is 1.3 seconds which is competitive with the state-of-the-art. The performance of the proposed model is compared with the standard approaches on two publicly available fundus image databases - DRIVE and STARE, using three popular metrics such as sensitivity, specificity, and accuracy.
AB - Retinal vasculature can be considered a major indicator of various opthalmologic disorders and diseases. Therefore, it is important to accurately detect and segment the retinal blood vessels. We present a novel technique for the segmentation of arteries and veins from a retinal fundus image. Local 2D image patch-based analysis using intensity gradients is used as the basis for detection of vessels. Two stages of preprocessing are implemented before detection for ease of approach. The proposed model carries several advantages over the state-of-the-art. The computational cost of the proposed model is low. Also, the average execution time per image is 1.3 seconds which is competitive with the state-of-the-art. The performance of the proposed model is compared with the standard approaches on two publicly available fundus image databases - DRIVE and STARE, using three popular metrics such as sensitivity, specificity, and accuracy.
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U2 - 10.1109/INDICON56171.2022.10039792
DO - 10.1109/INDICON56171.2022.10039792
M3 - Conference contribution
AN - SCOPUS:85149245934
T3 - INDICON 2022 - 2022 IEEE 19th India Council International Conference
BT - INDICON 2022 - 2022 IEEE 19th India Council International Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE India Council International Conference, INDICON 2022
Y2 - 24 November 2022 through 26 November 2022
ER -
