Segmentation of optic disk (OD) from retinal images is a crucial task for early detection of many eye diseases, including glaucoma and diabetic retinopathy. The main goal of this research is to facilitate early diagnosis of certain pathologies via fully automated segmentation of the OD from retinal images. We propose a deep learning-based technique to delineate the boundary of OD from retinal images of patients with diabetic retinopathy and diabetic macular edema. In our method, we first localized OD within a region of interest (ROI) using random forest (RF). The RF is an ensemble algorithm, which trains and combines multiple decision trees to produce a highly accurate classifier. We then used a convolutional neural network (CNN) based model to segment OD from chosen ROIs in the retinal images. The developed algorithm has been validated on 480,249 image patches extracted from 49 images of public Indian diabetic retinopathy image dataset (IDRiD). This dataset includes images with large variability in terms of the spatial location of OD and presence of other eye lesions that resemble the contrast of OD. Validation metrics including average of Dice and Jaccard indexes (DI and JI), Hausdorff distance (HD), and absolute surface difference (ASD) were reported as 82.62 ± 11.07%, 71.78 ± 14.87%, 13.19 ± 10.90 mm, and 22.74 ± 19.78%, respectively. As compared to other alternative methods, such as K-nearest neighbors (KNN), deformable models, graph-cuts, and image thresholding, our method yielded higher accuracy for OD segmentation in comparison to manual expert delineation. The algorithm-generated results demonstrate the usefulness of our proposed method for automated segmentation of OD from retinal images.

Additional Metadata
Keywords Convolutional neural network, Diabetic retinopathy, Glaucoma, Optic disk, Random forest
Persistent URL dx.doi.org/10.1117/12.2512239
Conference Medical Imaging 2019: Computer-Aided Diagnosis
Citation
Zabihollahy, F. (F.), & Ukwatta, E.M. (2019). Fully-automated segmentation of optic disk from retinal images using deep learning techniques. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. doi:10.1117/12.2512239