TY - GEN
T1 - Automated detection of neovascularization for proliferative diabetic retinopathy screening
AU - Roychowdhury, Sohini
AU - Koozekanani, Dara D.
AU - Parhi, Keshab K.
PY - 2016/10/13
Y1 - 2016/10/13
N2 - Neovascularization is the primary manifestation of proliferative diabetic retinopathy (PDR) that can lead to acquired blindness. This paper presents a novel method that classifies neovascularizations in the 1-optic disc (OD) diameter region (NVD) and elsewhere (NVE) separately to achieve low false positive rates of neovascularization classification. First, the OD region and blood vessels are extracted. Next, the major blood vessel segments in the 1-OD diameter region are classified for NVD, and minor blood vessel segments elsewhere are classified for NVE. For NVD and NVE classifications, optimal region-based feature sets of 10 and 6 features, respectively, are used. The proposed method achieves classification sensitivity, specificity and accuracy for NVD and NVE of 74%, 98.2%, 87.6%, and 61%, 97.5%, 92.1%, respectively. Also, the proposed method achieves 86.4% sensitivity and 76% specificity for screening images with PDR from public and local data sets. Thus, the proposed NVD and NVE detection methods can play a key role in automated screening and prioritization of patients with diabetic retinopathy.
AB - Neovascularization is the primary manifestation of proliferative diabetic retinopathy (PDR) that can lead to acquired blindness. This paper presents a novel method that classifies neovascularizations in the 1-optic disc (OD) diameter region (NVD) and elsewhere (NVE) separately to achieve low false positive rates of neovascularization classification. First, the OD region and blood vessels are extracted. Next, the major blood vessel segments in the 1-OD diameter region are classified for NVD, and minor blood vessel segments elsewhere are classified for NVE. For NVD and NVE classifications, optimal region-based feature sets of 10 and 6 features, respectively, are used. The proposed method achieves classification sensitivity, specificity and accuracy for NVD and NVE of 74%, 98.2%, 87.6%, and 61%, 97.5%, 92.1%, respectively. Also, the proposed method achieves 86.4% sensitivity and 76% specificity for screening images with PDR from public and local data sets. Thus, the proposed NVD and NVE detection methods can play a key role in automated screening and prioritization of patients with diabetic retinopathy.
KW - Diabetic retinopathy
KW - Frangi-filter
KW - Neovascularization
KW - Watershed transform
UR - http://www.scopus.com/inward/record.url?scp=85009136676&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009136676&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2016.7590945
DO - 10.1109/EMBC.2016.7590945
M3 - Conference contribution
C2 - 28268564
AN - SCOPUS:85009136676
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1300
EP - 1303
BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Y2 - 16 August 2016 through 20 August 2016
ER -
