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A self-adaptive distance regularized level set evolution method for optical disk segmentation.

Huiqun Wu¹, Xingyun Geng², Xiaofeng Zhang²

¹Department of Medical Informatics, Medical School of Nantong University, Jiangsu 226001, China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Fudan University, Shanghai 200032, China.

Bio-Medical Materials and Engineering

|September 18, 2014

Summary

This study introduces a novel self-adaptive level set evolution method for precise optic disc (OD) segmentation in retinal images. This automated approach aids in early disease detection, improving efficiency for ophthalmologists.

Keywords:

Optic disk imaging informatics level set evolution retinal imaging

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

The optic disc (OD) is a critical retinal structure where changes in shape and area can signify disease.
Accurate quantification of the optic disc is essential for diagnosing eye conditions.
Current segmentation methods may require manual intervention or complex re-initialization steps.

Purpose of the Study:

To develop and validate a self-adaptive distance regularized level set evolution method for automated optic disc segmentation.
To improve the efficiency and accuracy of optic disc segmentation in eye fundus images.
To provide a tool for computer-aided early detection of glaucoma and retinopathy.

Main Methods:

Image preprocessing using Fourier correlation coefficient filtering to define initial contours.
Application of a self-adaptive distance regularized level set evolution for accurate optic disc boundary detection.
Validation using 100 eye fundus color images from a public database.

Main Results:

The proposed method successfully segmented the optic disc without periodic re-initialization.
Achieved accurate boundary detection of the optic disc.
Demonstrated the algorithm's effectiveness on a diverse set of fundus images.

Conclusions:

The developed automatic optic disc segmentation method enhances efficiency for ophthalmologists.
This technique holds significant potential for computer-aided early diagnosis of glaucoma and retinopathy.
The self-adaptive level set evolution offers a robust solution for optic disc quantification.