TY - GEN
T1 - Fundus image texture features analysis in diabetic retinopathy diagnosis
AU - Sarwinda, Devvi
AU - B., Alhadi
AU - Arymurthy, Aniati Murni
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - This paper investigates texture feature capabilities from fundus images to differentiate between diabetic retinopathy (DR), age-related macular degeneration (AMD) screening and normal. Our proposed method using improvement of local binary pattern (LBP) with calculation of LBP original value and magnitude value of fundus images. This method is compared with Local Line Binary Pattern (LLBP). In this study, four experiments (DR-Normal, DR-AMD, AMD-Normal, Multiclass) were designed for two databases, DIARETDB0 database and STARE. Kernel PCA is choosed as feature selection method, and three classifiers are tested (Naive Bayes, SVM, and KNN). The experimental results show that our proposed method has higher accuracy than LLBP, with accuracy of binary classification 100% for DR-Normal and AMD-Normal. While, multiclass classification (DR-AMD-Normal) achieves an accuracy 80-84%. These results suggest that our proposed method in this paper can be useful in a diagnosis aid system for diabetic retinopathy.
AB - This paper investigates texture feature capabilities from fundus images to differentiate between diabetic retinopathy (DR), age-related macular degeneration (AMD) screening and normal. Our proposed method using improvement of local binary pattern (LBP) with calculation of LBP original value and magnitude value of fundus images. This method is compared with Local Line Binary Pattern (LLBP). In this study, four experiments (DR-Normal, DR-AMD, AMD-Normal, Multiclass) were designed for two databases, DIARETDB0 database and STARE. Kernel PCA is choosed as feature selection method, and three classifiers are tested (Naive Bayes, SVM, and KNN). The experimental results show that our proposed method has higher accuracy than LLBP, with accuracy of binary classification 100% for DR-Normal and AMD-Normal. While, multiclass classification (DR-AMD-Normal) achieves an accuracy 80-84%. These results suggest that our proposed method in this paper can be useful in a diagnosis aid system for diabetic retinopathy.
KW - Diabetes mellitus
KW - Diabetic retinopathy
KW - Fundus images
KW - LBP
UR - http://www.scopus.com/inward/record.url?scp=85045614621&partnerID=8YFLogxK
U2 - 10.1109/ICSensT.2017.8304447
DO - 10.1109/ICSensT.2017.8304447
M3 - Conference contribution
AN - SCOPUS:85045614621
T3 - Proceedings of the International Conference on Sensing Technology, ICST
SP - 1
EP - 5
BT - 2017 11th International Conference on Sensing Technology, ICST 2017
PB - IEEE Computer Society
T2 - 11th International Conference on Sensing Technology, ICST 2017
Y2 - 4 December 2017 through 6 December 2017
ER -
