TY - GEN
T1 - Automatic Segmentation of Optic Disc Using Affine Snakes in Gradient Vector Field
AU - Dey, Sidhartha
AU - Tahiliani, Kapil
AU - Harish Kumar, J. R.
AU - Pediredla, A. K.
AU - Seelamantula, Chandra Sekhar
PY - 2019/5/1
Y1 - 2019/5/1
N2 - The optic disc is one of the prominent features of a retinal fundus image, and its segmentation is a critical component in automated retinal screening systems for ophthalmic anomalies, such as diabetic retinopathy and glaucoma. In this paper, we propose a novel method for optic disc segmentation using affine snakes, where the snake evolves using an affine transformation and requires a priori knowledge of the desired object shape. We determine the affine transformation parameters by first computing a force field on the image and then deforming the snake till the net force on the snake is zero. The affine snakes technique excels in its speed of convergence. This is attributed to the fact that only six parameters require optimization, the six parameters being the horizontal and vertical scaling, shearing and translation components of an affine transformation. Localization of the optic disc is done using normalized cross-correlation and segmentation is done using the affine snakes technique. This technique is tested on publicly available fundus image datasets, such as IDRiD, Drishti-GS, RIM-ONE, DRIONS-DB, and Messidor, with Dice In-dices of 0.943, 0.958, 0.933, 0.913, and 0.912, respectively.
AB - The optic disc is one of the prominent features of a retinal fundus image, and its segmentation is a critical component in automated retinal screening systems for ophthalmic anomalies, such as diabetic retinopathy and glaucoma. In this paper, we propose a novel method for optic disc segmentation using affine snakes, where the snake evolves using an affine transformation and requires a priori knowledge of the desired object shape. We determine the affine transformation parameters by first computing a force field on the image and then deforming the snake till the net force on the snake is zero. The affine snakes technique excels in its speed of convergence. This is attributed to the fact that only six parameters require optimization, the six parameters being the horizontal and vertical scaling, shearing and translation components of an affine transformation. Localization of the optic disc is done using normalized cross-correlation and segmentation is done using the affine snakes technique. This technique is tested on publicly available fundus image datasets, such as IDRiD, Drishti-GS, RIM-ONE, DRIONS-DB, and Messidor, with Dice In-dices of 0.943, 0.958, 0.933, 0.913, and 0.912, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85068979384&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068979384&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8682408
DO - 10.1109/ICASSP.2019.8682408
M3 - Conference contribution
AN - SCOPUS:85068979384
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1204
EP - 1208
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -
