A Retinal Image Enhancement Technique for Blood Vessel Segmentation Algorithm

TL;DR

This paper evaluates a spatially adaptive contrast enhancement method for retinal images, demonstrating its superiority in blood vessel segmentation accuracy over other techniques on public datasets.

Contribution

It introduces and assesses a novel contrast enhancement technique integrated with an improved vessel segmentation algorithm, outperforming existing methods.

Findings

The proposed method outperforms five existing contrast enhancement techniques.

It achieves higher segmentation accuracy on STARE and DRIVE datasets.

The enhancement technique improves the reliability of retinal blood vessel segmentation.

Abstract

The morphology of blood vessels in retinal fundus images is an important indicator of diseases like glaucoma, hypertension and diabetic retinopathy. The accuracy of retinal blood vessels segmentation affects the quality of retinal image analysis which is used in diagnosis methods in modern ophthalmology. Contrast enhancement is one of the crucial steps in any of retinal blood vessel segmentation approaches. The reliability of the segmentation depends on the consistency of the contrast over the image. This paper presents an assessment of the suitability of a recently invented spatially adaptive contrast enhancement technique for enhancing retinal fundus images for blood vessel segmentation. The enhancement technique was integrated with a variant of Tyler Coye algorithm, which has been improved with Hough line transformation based vessel reconstruction method. The proposed approach was evaluated on two public datasets STARE and DRIVE. The assessment was done by comparing the segmentation performance with five widely used contrast enhancement techniques based on wavelet transform, contrast limited histogram equalization, local normalization, linear un-sharp masking and contourlet transform. The results revealed that the assessed enhancement technique is well suited for the application and also outperforms all compared techniques.