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Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual

Kemal Akyol¹, Baha Şen², Şafak Bayır¹

¹Department of Computer Engineering, Karabük University, 78050 Karabük, Turkey.

Computational and Mathematical Methods in Medicine

|April 26, 2016

Summary

Accurate optic disc localization in retinal images is crucial for diagnosing diabetic retinopathy. A new five-step method improves detection in noisy images, achieving over 90% accuracy on public datasets.

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Area of Science:

Ophthalmology
Computer Vision
Medical Image Analysis

Background:

Diabetic retinopathy diagnosis relies on identifying lesions like hard exudates and neovascularization.
Optic disc localization is a critical preprocessing step in automated eye disease diagnosis systems.
Detecting the optic disc in diseased retinal images, particularly those with diabetic retinopathy, presents challenges due to noise and similar visual features.

Purpose of the Study:

To develop and validate a novel, efficient, and accurate method for optic disc localization in retinal images.
To address the difficulties in optic disc detection caused by image noise and pathological lesions associated with diabetic retinopathy.

Main Methods:

The proposed approach integrates five key steps: image processing, keypoint extraction, texture analysis, visual dictionary creation, and classifier techniques.
The methodology is designed to be robust against image noise and artifacts commonly found in retinal scans.
Machine learning classifiers are employed to distinguish the optic disc from other image features.

Main Results:

The technique was evaluated on three publicly available datasets: DIARETDB1, DRIVE, and ROC.
Quantitative results demonstrated high average optic disc detection accuracies.
Achieved accuracies were 94.38% on DIARETDB1, 95.00% on DRIVE, and 90.00% on ROC.

Conclusions:

The presented novel approach offers an efficient and accurate solution for optic disc localization in challenging retinal images.
The method's effectiveness is validated by its high performance across multiple public datasets.
This technique contributes to the advancement of computer-aided diagnosis systems for diabetic retinopathy and other eye diseases.