TY - GEN
T1 - Segmentation of optic nerve head images
AU - Boonyakiat, Punsiri
AU - Silapachote, Piyanuch
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/5
Y1 - 2017/9/5
N2 - Segmentation of the optic nerve head or optic disc in digital retinal fundus photographs is a non-invasive procedure that plays an important role in early detection of abnormalities of the eyes, particularly glaucoma diseases. Developing an automatic system, we employ image processing techniques coupled with graph cut algorithms from combinatorial optimization. Avoiding the need of manual pre-segmentation for constructing an initial graph, a supervised learning approach is effectively and efficiently applied. Crucial information is extracted from a set of labeled binary masks and integrated into weight assignments of the edges of the graph. We associate the characteristically bell-shape of a Gaussian distribution with the rounded circular-shape of the optic disc. Our approach was validated and evaluated on the RIM-ONE open database. Segmentation is successful on 91.12% of the entire 169 images, achieving 91% sensitivity and 88% accuracy.
AB - Segmentation of the optic nerve head or optic disc in digital retinal fundus photographs is a non-invasive procedure that plays an important role in early detection of abnormalities of the eyes, particularly glaucoma diseases. Developing an automatic system, we employ image processing techniques coupled with graph cut algorithms from combinatorial optimization. Avoiding the need of manual pre-segmentation for constructing an initial graph, a supervised learning approach is effectively and efficiently applied. Crucial information is extracted from a set of labeled binary masks and integrated into weight assignments of the edges of the graph. We associate the characteristically bell-shape of a Gaussian distribution with the rounded circular-shape of the optic disc. Our approach was validated and evaluated on the RIM-ONE open database. Segmentation is successful on 91.12% of the entire 169 images, achieving 91% sensitivity and 88% accuracy.
UR - https://www.scopus.com/pages/publications/85031749152
U2 - 10.1109/JCSSE.2017.8025902
DO - 10.1109/JCSSE.2017.8025902
M3 - Conference contribution
AN - SCOPUS:85031749152
T3 - Proceedings of the 2017 14th International Joint Conference on Computer Science and Software Engineering, JCSSE 2017
BT - Proceedings of the 2017 14th International Joint Conference on Computer Science and Software Engineering, JCSSE 2017
A2 - Chantamunee, Siripinyo
A2 - Doung-in, Suthanya
A2 - Thanathamathee, Putthiporn
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Joint Conference on Computer Science and Software Engineering, JCSSE 2017
Y2 - 12 July 2017 through 14 July 2017
ER -