Name
Abstract | ||
|---|---|---|
Glaucoma is a chronic eye disease which results in irreversible vision loss, and the optic cup-to-disc ratio (CDR) is an essential clinical indicator in diagnosing glaucoma, which means precise optic disc (OD) and optic cup (OC) segmentation become an important task. In this paper, we propose an automatic CDR measurement method. The method includes three stages: OD localization and ROI extraction, simultaneous segmentation of OD and OC, and CDR calculation. In the first stage, the morphological operation and the sliding window are combined to find the OD location and extract the ROI region. In the second stage, an improved deep neural network, named U-Net+CP+FL, which consists of U-shape convolutional architecture, a novel concatenating path and a multi-label fusion loss function, is adopted to simultaneously segment the OD and OC. Based on the segmentation results, the CDR value can be calculated in the last stage. Experimental results on the retinal images from public databases demonstrate that the proposed method can achieve comparable performance with ophthalmologist and superior performance when compared with other existing methods. Thus, our method can be a suitable tool for automated glaucoma analysis. |
Year | Venue | Field |
|---|---|---|
2018 | PRCV | Computer vision,Glaucoma,Sliding window protocol,Segmentation,Computer science,Cup-to-disc ratio,Optic disc,Artificial intelligence,Optic cup (anatomical),Retinal,Artificial neural network |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
5 | 5 | |