TY - GEN
T1 - Exudate detection in retinal fundus images using combination of mathematical morphology and Renyi entropy thresholding
AU - Qomariah, Dinial Utami Nurul
AU - Tjandrasa, Handayani
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/19
Y1 - 2018/1/19
N2 - Diabetic retinopathy (DR) is a microvascular complication of diabetes, causing abnormalities in the retina, and it is can cause blindness. Diabetic retinopathy can be detected by the appearance of hard exudates. Hard exudates are lipid formations leaking from these weakened blood vessels. Automatic detection of exudates is an early handler to diagnose diabetic retinopathy. This research proposed automatic detection of exudates using Renyi entropy thresholding and mathematical morphology. Renyi entropy thresholding has a controlling variable so that the obtained threshold value is more optimal. The proposed method using Renyi entropy thresholding and mathematical morphology has three stages: (1) preprocessing using contrast enhancement, (2) initial exudates detection based on mathematical morphology, and (3) exudates detection based on Renyi entropy thresholding. The test was performed using measurement evaluation method, sensitivity, specificity, and accuracy were 85.06%, 99.63%, and 99.54% respectively.
AB - Diabetic retinopathy (DR) is a microvascular complication of diabetes, causing abnormalities in the retina, and it is can cause blindness. Diabetic retinopathy can be detected by the appearance of hard exudates. Hard exudates are lipid formations leaking from these weakened blood vessels. Automatic detection of exudates is an early handler to diagnose diabetic retinopathy. This research proposed automatic detection of exudates using Renyi entropy thresholding and mathematical morphology. Renyi entropy thresholding has a controlling variable so that the obtained threshold value is more optimal. The proposed method using Renyi entropy thresholding and mathematical morphology has three stages: (1) preprocessing using contrast enhancement, (2) initial exudates detection based on mathematical morphology, and (3) exudates detection based on Renyi entropy thresholding. The test was performed using measurement evaluation method, sensitivity, specificity, and accuracy were 85.06%, 99.63%, and 99.54% respectively.
KW - Exudate
KW - Mathematical Morphology
KW - Renyi entropy thresholding
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85050534246&partnerID=8YFLogxK
U2 - 10.1109/ICTS.2017.8265642
DO - 10.1109/ICTS.2017.8265642
M3 - Conference contribution
AN - SCOPUS:85050534246
T3 - Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
SP - 31
EP - 36
BT - Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Information and Communication Technology and System, ICTS 2017
Y2 - 31 October 2017 through 31 October 2017
ER -
