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Fast and automatic algorithm for optic disc extraction in retinal images using principle-component-analysis-based

Saleh Shahbeig¹, Hossein Pourghassem

¹Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, 517, Najafabad, Isfahan, Iran.

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|March 5, 2013

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This study presents a fast, automatic algorithm for optic nerve (ON) head extraction from retinal images, achieving high accuracy without using blood-vessel data. The method effectively enhances image contrast and compensates for illumination changes for precise ON region detection.

Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Accurate optic nerve (ON) head extraction is crucial for diagnosing retinal diseases and for biometric identification.
Existing methods often rely on blood-vessel information, which can be unreliable due to image degradation.

Purpose of the Study:

To develop a fast and automatic algorithm for precise optic nerve (ON) head extraction from retinal images.
To achieve accurate ON region detection without utilizing blood-vessel information.

Main Methods:

An adaptive correction function is applied to curvelet transform coefficients to enhance image contrast and compensate for illumination variations.
Morphology operators based on geodesic conversions and a novel criterion are used for ON region detection.

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Local thresholding is employed for the final extraction of the ON region.

Main Results:

The algorithm achieved 100% accuracy in optic nerve (ON) head extraction on the DRIVE database.
The method demonstrated 97.53% accuracy on the STARE database.
The proposed algorithm successfully extracts the ON region without relying on blood-vessel data.

Conclusions:

The developed algorithm provides a robust and accurate method for optic nerve (ON) head extraction.
This technique offers a valuable tool for retinal disease diagnosis and biometric applications.
The method's independence from blood-vessel information enhances its applicability in challenging imaging conditions.