Computer-aided Glaucoma Diagnosis using Stochastic Watershed Transformation on Single Fundus Images

[EN] Glaucoma is a chronic eye disease and one of the major causes of permanent blindness. Since it does not show initial symptoms, early diagnosis is important to limit its progression. This paper presents an automatic optic nerve characterization algorithm for glaucoma diagnosis based only on retinal fundus images. For optic cup segmentation, we used a new method based on the stochastic watershed transformation applied on the YIQ colour space to extract clinical indicators such as the Cup/Disc ratio, the area Cup/Disc ratio and the ISNT rule. Afterwards, an assessment between normal and glaucomatous fundus images is performed. The proposed algorithm was evaluated on 6 different (private and public) databases containing 723 images (377 normal and 346 glaucomatous images) which achieved a specificity and sensitivity of 0.674 and 0.675, respectively. Moreover, an F-score of 0.770 was obtained when evaluating this method on the publicly available database Drishti-GS1. A comparison of the proposed work with other state-of-the-art methods demonstrates the robustness of the proposed algorithm; because it was tested using images from different databases with high variability, which is a common issue in this area. Additional comparisons with existing works for cup segmentation, that use the publicly available database Drishti-GS1, are also presented in this paper.The authors would like to thank K. Narasimhan from SASTRA University for facilitating access to their database and gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. This work was supported by the Project GALAHAD [H2020-ICT-2016-2017, 732613]. In particular, the work of Andres Diaz-Pinto has been supported by the Generalitat Valenciana under the scholarship Santiago Grisolia [GRISOLIA/2015/027].Díaz-Pinto, AY.; Morales, S.; Naranjo Ornedo, V.; Navea, A. (2019). Computer-aided Glaucoma Diagnosis using Stochastic Watershed Transformation on Single Fundus Images. Journal of Medical Imaging and Health Informatics (Online). 9(6):1057-1065. https://doi.org/10.1166/jmihi.2019.2721105710659