Peer Reviewed

1

DOI

10.17077/omia.1040

Conference Location

Athens, Greece

Publication Date

October 2016

Abstract

Accurate segmentation of optic cup and disc in retinal fundus images is required to derive the cup-to-disc ratio (CDR) parameter which is the main indicator for Glaucoma assessment. In this paper, we propose a coupled regression method for accurate segmentation of optic cup and disc in retinal colour fundus image. The proposed coupled regression framework consists of a parameter regressor which directly predicts CDR from a given image, as well as an ensemble shape regressor which iteratively estimates the OD-OC boundary by taking into account the CDR estimated by the parameter regressor. The parameter regressor and the shape regressor are then coupled together within a feedback loop so that estimation of one reinforces the other. Both parameter regressor and the ensemble shape regressor are modeled using Boosted Regression Trees. The proposed optic cup and disc segmentation method is applied on an image set of 50 patients and demonstrates high segmentation accuracy. A comparative study shows that our proposed method outperforms state of the art methods for cup segmentation.

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Copyright © 2016 the authors

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Oct 21st, 12:00 AM Oct 21st, 12:00 AM

Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression

Athens, Greece

Accurate segmentation of optic cup and disc in retinal fundus images is required to derive the cup-to-disc ratio (CDR) parameter which is the main indicator for Glaucoma assessment. In this paper, we propose a coupled regression method for accurate segmentation of optic cup and disc in retinal colour fundus image. The proposed coupled regression framework consists of a parameter regressor which directly predicts CDR from a given image, as well as an ensemble shape regressor which iteratively estimates the OD-OC boundary by taking into account the CDR estimated by the parameter regressor. The parameter regressor and the shape regressor are then coupled together within a feedback loop so that estimation of one reinforces the other. Both parameter regressor and the ensemble shape regressor are modeled using Boosted Regression Trees. The proposed optic cup and disc segmentation method is applied on an image set of 50 patients and demonstrates high segmentation accuracy. A comparative study shows that our proposed method outperforms state of the art methods for cup segmentation.