  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.
