TY - GEN
T1 - A unified approach for detection of diagnostically significant regions-of-interest in retinal fundus images
AU - Kumar, J. R.Harish
AU - Sachi, Simran
AU - Chaudhury, Kunaljit
AU - Harsha, S.
AU - Singh, Birendra Kumar
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/19
Y1 - 2017/12/19
N2 - Automatic detection of optic disc and fovea is a precursor to the computer-aided analysis of retinal pathologies. In this paper, we present a unified approach for optic disc and fovea detection based on normalized cross-correlation technique. The algorithm performance is optimized by introducing vector inner products and norms instead of conventional mean and variance computations. We report optic disc detection results on four publicly available fundus image databases amounting to a total of 1451 fundus images and fovea detection results on another four publicly available fundus image databases amounting to a total of 1454 fundus images. The proposed method results in an optic disc detection accuracy of 99.01%, 95.67%, 99.09%, and 100% on DRISHTI-GS, MESSIDOR, DRIONS-DB, and DRIVE fundus image databases, respectively, and fovea detection accuracy of 94.83%, 84.62%, 95.51%, and 97.14% on MESSIDOR, DIARETDB0, DIARETDB1, and DRIVE fundus image databases, respectively. The speed of optic disc and fovea detection has been improved considerably by downsampling technique. In addition, we report the effect of downsampling on the detection accuracy.
AB - Automatic detection of optic disc and fovea is a precursor to the computer-aided analysis of retinal pathologies. In this paper, we present a unified approach for optic disc and fovea detection based on normalized cross-correlation technique. The algorithm performance is optimized by introducing vector inner products and norms instead of conventional mean and variance computations. We report optic disc detection results on four publicly available fundus image databases amounting to a total of 1451 fundus images and fovea detection results on another four publicly available fundus image databases amounting to a total of 1454 fundus images. The proposed method results in an optic disc detection accuracy of 99.01%, 95.67%, 99.09%, and 100% on DRISHTI-GS, MESSIDOR, DRIONS-DB, and DRIVE fundus image databases, respectively, and fovea detection accuracy of 94.83%, 84.62%, 95.51%, and 97.14% on MESSIDOR, DIARETDB0, DIARETDB1, and DRIVE fundus image databases, respectively. The speed of optic disc and fovea detection has been improved considerably by downsampling technique. In addition, we report the effect of downsampling on the detection accuracy.
UR - https://www.scopus.com/pages/publications/85044214063
UR - https://www.scopus.com/pages/publications/85044214063#tab=citedBy
U2 - 10.1109/TENCON.2017.8227829
DO - 10.1109/TENCON.2017.8227829
M3 - Conference contribution
AN - SCOPUS:85044214063
VL - 2017-December
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 19
EP - 24
BT - TENCON 2017 - 2017 IEEE Region 10 Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Region 10 Conference, TENCON 2017
Y2 - 5 November 2017 through 8 November 2017
ER -