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Retinal blood vessel segmentation from fundus image using an efficient multiscale directional representation

Rafsanjany Kushol^1,2, Md Hasanul Kabir², M Abdullah-Al-Wadud³

¹Department of Computing Science, University of Alberta, Edmonton, AB, Canada.

Mathematical Biosciences and Engineering : MBE

|December 31, 2020

Summary

Accurate retinal blood vessel segmentation is crucial for diagnosing eye diseases and preventing vision loss. This study introduces an efficient method using Bendlet transform and ensemble classifiers, achieving ~95% accuracy on benchmark datasets.

Keywords:

Bendlets contrast enhancement ensemble classifier medical image segmentation multi-resolution multi-scale transform retinal blood vessel

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Retinal blood vessel abnormalities are a primary cause of vision impairment and blindness.
Accurate segmentation of retinal vessels is vital for early diagnosis of diseases like diabetic retinopathy and for biometric applications.

Purpose of the Study:

To develop an efficient and robust method for retinal blood vessel segmentation.
To improve the accuracy of automated screening and diagnosis of ophthalmologic conditions.

Main Methods:

A 4-D feature vector was constructed using the Bendlet transform for enhanced directional information capture.
Ensemble classifiers were employed to classify pixels as either vessel or non-vessel segments.

Main Results:

The proposed approach achieved an average accuracy of approximately 95% for retinal blood vessel segmentation.
Experiments on the DRIVE and STARE benchmark datasets demonstrated the effectiveness and robustness of the method.

Conclusions:

The Bendlet transform-based approach offers superior performance in retinal blood vessel segmentation compared to traditional methods.
This technique enhances the potential for early detection and treatment of vision-threatening eye diseases.