Fuzzy Clustering Based Glaucoma Detection Using the CDR

Devasia, Thresiamma; Jacob, Poulose; Thomas, Tessamma

doi:10.5121/sipij.2015.6305

Cited by 2 publications

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Robust segmentation of optic disc and optic cup using statistical Kurtosis test

Biswal¹,

Eadara²,

Bebarta³

et al. 2019

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Glaucoma is a chronic and irreversible eye disease that leads to the loss of vision. Evaluation of the Cup to Disc Ratio (CDR) plays a prominent role in the early detection of glaucoma. This paper presents a novel algorithm to compute the CDR for the fundus images. In order to calculate the CDR, the vertical diameter of Optic Disc (OD) and the vertical diameter of the Optic Cup (OC) are calculated from the segmented OD and segmented OC, respectively. This study presents OD and OC segmentation algorithms based on a new statistics‐based kurtosis test. A unique OC boundary segmentation method is presented, which is the combination of partial cup extraction and the cup boundary under the Central Retinal Blood vessels (CRBV). A novel preprocessing technique is introduced to extract the CRBV from the automated Region of Interest (ROI). The experimental results confirm that the proposed algorithm outperforms the state‐of‐art OD and OC segmentation on the three publicly available datasets: ORIGA, DIARETDB0, and DIARETDB1. The proposed OD and OC segmentation achieve accuracies of up to 0.99 and 0.97, respectively. In addition, the proposed model achieves excellent CDR evaluation with an average error percentage reduced to up to 9.6496 for the considered datasets.

show abstract