TY - GEN
T1 - Segmentation of Optic Nerve Head Using Two-stage Snakes in Generalized Gradient Vector Field
AU - Harish Kumar, J. R.
AU - Dutta, Sayan
AU - Sonthalia, Avinash
AU - Pai, Namratha
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 - We propose a generalized gradient vector field based two-stage affine snakes method for the segmentation of optic nerve head in retinal fundus images. We obtain the course segmentation using affine transformation of an elliptical shape prior that tends to converge iteratively based on the force computations in generalized gradient vector field of the fundus image. We optimize six affine parameters out of which two for scaling, two for shearing, and two for translation of the shape prior. We obtain the fine segmentation using point snakes algorithm. The two-stage idea resulted in fast convergence of the snake on the optic disc with great segmentation accuracy. The detection of the optic nerve head is done using normalized template matching technique. The proposed method is validated on various publicly available fundus image datasets such as Drions-DB, IDRiD, RIM-ONE, Messidor, and Drishti-GS, resulting in Dice similarity index of 0.91, 0.94, 0.94, 0.91 and 0.95, respectively.
AB - We propose a generalized gradient vector field based two-stage affine snakes method for the segmentation of optic nerve head in retinal fundus images. We obtain the course segmentation using affine transformation of an elliptical shape prior that tends to converge iteratively based on the force computations in generalized gradient vector field of the fundus image. We optimize six affine parameters out of which two for scaling, two for shearing, and two for translation of the shape prior. We obtain the fine segmentation using point snakes algorithm. The two-stage idea resulted in fast convergence of the snake on the optic disc with great segmentation accuracy. The detection of the optic nerve head is done using normalized template matching technique. The proposed method is validated on various publicly available fundus image datasets such as Drions-DB, IDRiD, RIM-ONE, Messidor, and Drishti-GS, resulting in Dice similarity index of 0.91, 0.94, 0.94, 0.91 and 0.95, respectively.
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U2 - 10.1109/INDICON56171.2022.10039761
DO - 10.1109/INDICON56171.2022.10039761
M3 - Conference contribution
AN - SCOPUS:85149199025
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 -
