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Automated optic disk boundary detection by modified active contour model.

Juan Xu¹, Opas Chutatape, Paul Chew

¹Department of Ophthalmology, School of Medicine, University of Pittsburgh, 203 Lothrop Street, EEI-834, Pittsburgh, PA 15213, USA. xxujuan@pmail.ntu.edu.sg

IEEE Transactions on Bio-Medical Engineering

|March 16, 2007

Summary

This study introduces an improved snake algorithm for automated optic disk detection in fundus images. The novel method enhances accuracy and robustness, outperforming existing techniques for precise boundary estimation.

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Accurate optic disk segmentation is crucial for diagnosing various eye conditions.
Existing methods like Gradient Vector Flow Snake (GVF-snake) and Active Shape Models (ASM) face challenges with image noise, occlusions, and ill-defined boundaries.

Purpose of the Study:

To develop a novel, fully automated algorithm for optic disk boundary detection in fundus images.
To improve the accuracy and robustness of optic disk segmentation compared to existing methods.

Main Methods:

A deformable model-based algorithm extending the traditional snake model.
Incorporation of clustering for contour point separation into edge and uncertain groups.
A combined local and global contour update strategy balancing stability and accuracy.

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Main Results:

The proposed algorithm achieved high accuracy, with Mean Distance to Closest Point (MDCP) <3 pixels on 100 test images.
Demonstrated superior performance and robustness against blood vessel occlusions, noise, and fuzzy boundaries.
Outperformed GVF-snake and modified ASM in comparative evaluations.

Conclusions:

The enhanced snake algorithm provides a more accurate and robust solution for automated optic disk boundary detection.
This method holds potential for improving automated analysis in retinal imaging and ophthalmic diagnostics.