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Precise segmentation of the bulbar conjunctiva for hyperaemia images., , , , and . Pattern Anal. Appl., 21 (2): 563-577 (2018)On the analysis of local and global features for hyperemia grading., , , , , and . ICMV, volume 10341 of SPIE Proceedings, page 103411T. SPIE, (2016)A Novel Framework for Hyperemia Grading Based on Artificial Neural Networks., , , and . IWANN (1), volume 9094 of Lecture Notes in Computer Science, page 263-275. Springer, (2015)Automatic identification of vessel crossovers in retinal images., , , and . ICMV, volume 9445 of SPIE Proceedings, page 94451G. SPIE, (2014)Automatic Selection of Video Frames for Hyperemia Grading., , , , and . EUROCAST, volume 9520 of Lecture Notes in Computer Science, page 479-486. Springer, (2015)Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar Conjunctiva., , , , and . Comp. Math. Methods in Medicine, (2016)Comparing Machine Learning Techniques in a Hyperemia Grading Framework., , , , and . ICAART (2), page 423-429. SciTePress, (2016)On the development of conjunctival hyperemia computer-assisted diagnosis tools: Influence of feature selection and class imbalance in automatic gradings., , , , , and . Artificial Intelligence in Medicine, (2016)On the analysis of feature selection techniques in a conjunctival hyperemia grading framework., , , , , and . ESANN, (2016)