An adaptive thresholding approach for automatic optic disk segmentation

TL;DR

This paper introduces an adaptive thresholding algorithm for automatic optic disk segmentation in retinal images, improving robustness to illumination changes and image variability, with promising results on standard datasets.

Contribution

The paper presents a novel local adaptive thresholding method for optic disk segmentation that enhances accuracy and robustness over existing techniques.

Findings

Achieved 98% correct detection rate in KHATAM database.

Mean overlap of 43.21% in DRIVE database.

Robustness to illumination variations demonstrated.

Abstract

Optic disk segmentation is a prerequisite step in automatic retinal screening systems. In this paper, we propose an algorithm for optic disk segmentation based on a local adaptive thresholding method. Location of the optic disk is validated by intensity and average vessel width of retinal images. Then an adaptive thresholding is applied on the temporal and nasal part of the optic disc separately. Adaptive thresholding, makes our algorithm robust to illumination variations and various image acquisition conditions. Moreover, experimental results on the DRIVE and KHATAM databases show promising results compared to the recent literature. In the DRIVE database, the optic disk in all images is correctly located and the mean overlap reached to 43.21%. The optic disk is correctly detected in 98% of the images with the mean overlap of 36.32% in the KHATAM database.