TY - GEN
T1 - Quality assessment of eye fundus images taken by wide-view non-mydriatic cameras
AU - Carrillo, Cesar
AU - Calderon, Gustavo
AU - Lopez, Osvaldo
AU - Nakano, Marko
AU - Perez-Meana, Hector
AU - Perez, Anri
AU - Quiroz, Hugo
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Non-Mydriatic fundus cameras are very suitable for teleophthalmology framework, because this type of fundus cameras does not require eye-drop to dilate patient's pupil to take image, and then image acquisition can be realized without expert such as ophthalmologist. However, in this scheme, automatic and accurate image quality assessment on site is indispensable, because low-quality images are useless for reliable diagnostic. In this paper we analyze several generic features, such as statistic feature of histogram, cooccurrence matrix, run-length and Cumulative Probability of Blur Detection (CPBD), for automatic quality assessment of the fundus images taken by wide-view non-mydriatic fundus cameras. The performance of several combination of the generic features extracted from the fundus images are evaluated using different classifiers, such as support vector machine, K Nearest Neighbor (KNN) classifier, decision tree-based classifiers, etc.
AB - Non-Mydriatic fundus cameras are very suitable for teleophthalmology framework, because this type of fundus cameras does not require eye-drop to dilate patient's pupil to take image, and then image acquisition can be realized without expert such as ophthalmologist. However, in this scheme, automatic and accurate image quality assessment on site is indispensable, because low-quality images are useless for reliable diagnostic. In this paper we analyze several generic features, such as statistic feature of histogram, cooccurrence matrix, run-length and Cumulative Probability of Blur Detection (CPBD), for automatic quality assessment of the fundus images taken by wide-view non-mydriatic fundus cameras. The performance of several combination of the generic features extracted from the fundus images are evaluated using different classifiers, such as support vector machine, K Nearest Neighbor (KNN) classifier, decision tree-based classifiers, etc.
KW - Fundus images
KW - Image quality assessment
KW - Machine learning
KW - Non-mydriatic fundus camera
KW - Teleophthalmology
UR - http://www.scopus.com/inward/record.url?scp=85083549997&partnerID=8YFLogxK
U2 - 10.1109/ROPEC48299.2019.9057034
DO - 10.1109/ROPEC48299.2019.9057034
M3 - Contribución a la conferencia
AN - SCOPUS:85083549997
T3 - 2019 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2019
BT - 2019 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2019
Y2 - 13 November 2019 through 15 November 2019
ER -
